OntologySearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from owlready2 import *\n",
    "import re\n",
    "import fasttext \n",
    "import numpy as np\n",
    "from numpy import dot\n",
    "from numpy.linalg import norm\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosineSimilarity(a, b):\n",
    "    return np.inner(a, b)/(norm(a)*norm(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Usage: split(name.lower) for name in class_names_list\n",
    "def delimSplit(string, delimiters = ['-','_'], maxsplit=0):\n",
    "    import re\n",
    "    regexPattern = '|'.join(map(re.escape, delimiters))\n",
    "    return [word for word in re.split(regexPattern, string, maxsplit) if len(word) > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessString(string):\n",
    "    string = string.lower()\n",
    "    string = re.sub(\"[^a-zA-Z]\", \" \", string)            # remove non alphabets with a space which is used as a delimiter later\n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "* Owlready2 * WARNING: AnnotationProperty http://www.semanticweb.org/ram/ontologies/2019/6/.owl#creator belongs to more than one entity types: [owl.ObjectProperty, owl.AnnotationProperty, .isAlgorithmConfigurationOf]; I'm trying to fix it...\n",
      "* Owlready2 * WARNING: DataProperty http://www.semanticweb.org/ram/ontologies/2019/6/.owl#revision belongs to more than one entity types: [owl.ObjectProperty, owl.DatatypeProperty, owl.topObjectProperty, owl.topDataProperty]; I'm trying to fix it...\n",
      "* Owlready2 * WARNING: AnnotationProperty http://www.semanticweb.org/ram/ontologies/2019/6/.owl#created belongs to more than one entity types: [owl.DatatypeProperty, owl.AnnotationProperty, owl.topDataProperty]; I'm trying to fix it...\n"
     ]
    }
   ],
   "source": [
    "file_prefix = 'file://'\n",
    "onto = get_ontology(file_prefix + \"AI_10-12-2019.owl\").load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_list = list(onto.classes())\n",
    "class_names_list = [c.get_name(c) for c in class_list]\n",
    "\n",
    "phraseToClassDict = {}\n",
    "for c in class_list:\n",
    "    phraseToClassDict[str(delimSplit(c.name.lower()))] = c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gloveSearch(query, model):\n",
    "    \"\"\" \n",
    "    Returns sorted list of class objects closest to the query using glove embeddings of their 'name' \n",
    "    \n",
    "    Parameters: \n",
    "    query (str): query \n",
    "    model: gensim glove model\n",
    "  \n",
    "    Returns: \n",
    "    list: list of class object and similarity score of the class name to the query.\n",
    "  \n",
    "    \"\"\"\n",
    "    query = preprocessString(query)\n",
    "    results = []\n",
    "    phrases = [delimSplit(c.lower()) for c in class_names_list]\n",
    "    for phrase in phrases:\n",
    "        tokens_1 = [p for p in phrase if p in model.vocab]\n",
    "        tokens_2 = [t for t in query.split() if t in model.vocab]\n",
    "\n",
    "        #compute cosine similarity using word embedings \n",
    "        cosine=0\n",
    "        if (len(tokens_1) and len(tokens_2)):\n",
    "            cosine=model.n_similarity(tokens_1,tokens_2)\n",
    "            results.append([phrase,cosine])\n",
    "    results = sorted(results, key = lambda x: x[1], reverse = True)\n",
    "    results = [[phraseToClassDict[str(result[0])], result[1]] for result in results]\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fastTextSearch(query, model):\n",
    "    \"\"\" \n",
    "    Returns sorted list of class objects closest to the query using fasttext embeddings of their 'name' \n",
    "    \n",
    "    Parameters: \n",
    "    query (str): query \n",
    "    model: fasttext model with subword information\n",
    "  \n",
    "    Returns: \n",
    "    list: list of class object and similarity score of the class name to the query.\n",
    "  \n",
    "    \"\"\"\n",
    "    query = preprocessString(query)\n",
    "    results = []\n",
    "    phrases = [delimSplit(c.lower()) for c in class_names_list]\n",
    "    phrases = [' '.join(word) for word in phrases]\n",
    "    for phrase in phrases:\n",
    "        similarityScore = cosineSimilarity(model.get_sentence_vector(phrase), model.get_sentence_vector(query))\n",
    "        results.append([phrase,similarityScore])\n",
    "    results = sorted(results, key = lambda x: x[1], reverse = True)\n",
    "    results = [[phraseToClassDict[str(list(result[0].split()))], result[1]] for result in results]\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'Andrew NG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader as api\n",
    "gloveModel = api.load(\"glove-wiki-gigaword-100\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_glove = gloveSearch(query, gloveModel)\n",
    "for result in results_glove:\n",
    "    print('Class name: ', result[0].name, '. Score: ', result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fasttextModel = fasttext.load_model('crawl-300d-2M-subword.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class name:  f-GAN . Score:  0.63803226\n",
      "Class name:  Julia . Score:  0.5634246\n",
      "Class name:  Lempel-Ziv-Welch_algorithm . Score:  0.51531863\n",
      "Class name:  k-NN . Score:  0.5115085\n",
      "Class name:  Theil-Sen_estimator . Score:  0.508926\n",
      "Class name:  Adam . Score:  0.49491572\n",
      "Class name:  Dempster-Shafer_Theory . Score:  0.48805767\n",
      "Class name:  Q-Learning . Score:  0.48356014\n",
      "Class name:  K-Means_Clustering . Score:  0.4741715\n",
      "Class name:  Wasserstein_GAN . Score:  0.47088957\n",
      "Class name:  n-gram_analysis . Score:  0.4693178\n",
      "Class name:  t-SNE . Score:  0.46721533\n",
      "Class name:  GAN . Score:  0.46471044\n",
      "Class name:  MAGAN . Score:  0.46277606\n",
      "Class name:  R_Language . Score:  0.4563136\n",
      "Class name:  Neural_MT . Score:  0.4557787\n",
      "Class name:  SQ-RBF_Function . Score:  0.44125172\n",
      "Class name:  Conditional_GAN_(CGAN) . Score:  0.43948233\n",
      "Class name:  Interlingual_MT . Score:  0.4383302\n",
      "Class name:  Hybrid_MT . Score:  0.43297178\n",
      "Class name:  Laplacian_Pyramid_GAN_(LAPGAN) . Score:  0.43083298\n",
      "Class name:  C_Language . Score:  0.43038794\n",
      "Class name:  3-D_Reconstruction . Score:  0.42822486\n",
      "Class name:  Huber_Regression . Score:  0.42752853\n",
      "Class name:  Nouvelle_AI . Score:  0.42625067\n",
      "Class name:  ICA . Score:  0.42594522\n",
      "Class name:  Huber_Loss . Score:  0.41123617\n",
      "Class name:  Statistical_AI . Score:  0.40982133\n",
      "Class name:  Boundary_Seeking_GAN . Score:  0.40672377\n",
      "Class name:  Statistical_based_MT . Score:  0.4061075\n",
      "Class name:  AI_Concepts . Score:  0.4050803\n",
      "Class name:  Light_GBM . Score:  0.40281722\n",
      "Class name:  AI_Approaches . Score:  0.40252438\n",
      "Class name:  AI_Methodoogy . Score:  0.40210205\n",
      "Class name:  Coupled_GAN . Score:  0.40147382\n",
      "Class name:  Example-based_MT . Score:  0.400685\n",
      "Class name:  Vanilla_GAN . Score:  0.39944443\n",
      "Class name:  Miscellaneous_AI_applications . Score:  0.3990181\n",
      "Class name:  Dictionary-based_MT . Score:  0.3971449\n",
      "Class name:  AI_Ontology . Score:  0.39583167\n",
      "Class name:  Rule-based_MT . Score:  0.39346442\n",
      "Class name:  Super_Resolution_GAN_(SRGAN) . Score:  0.39022353\n",
      "Class name:  Transfer-based_MT . Score:  0.39005885\n",
      "Class name:  S-shaped_rectified_linear_activation_unit_(SReLU) . Score:  0.3892671\n",
      "Class name:  Haskell . Score:  0.38400388\n",
      "Class name:  Deep_Convolutional_GAN_(DCGAN) . Score:  0.38069576\n",
      "Class name:  PoS_Tagging . Score:  0.38019294\n",
      "Class name:  Softmax_GAN . Score:  0.37956417\n",
      "Class name:  Theano . Score:  0.3792686\n",
      "Class name:  Mode_Regularized_GAN . Score:  0.37401983\n",
      "Class name:  Least_Squares_GAN . Score:  0.37105027\n",
      "Class name:  Energy_Based_GAN . Score:  0.36993778\n",
      "Class name:  Improved_Training_for_Wasserstein_GAN . Score:  0.3690974\n",
      "Class name:  LARS_Lasso . Score:  0.36863357\n",
      "Class name:  SVD . Score:  0.36856207\n",
      "Class name:  CLAHE . Score:  0.3684092\n",
      "Class name:  The_Kalman_Filter . Score:  0.36599118\n",
      "Class name:  Conditional_VAE . Score:  0.36439028\n",
      "Class name:  Sub-symbolic_based_AI . Score:  0.36256257\n",
      "Class name:  Person_Re-Identification . Score:  0.35959136\n",
      "Class name:  Semi-Supervised_Person_Re-Identification . Score:  0.35745427\n",
      "Class name:  Weka . Score:  0.35739747\n",
      "Class name:  LightGBM . Score:  0.3567238\n",
      "Class name:  MX-Net . Score:  0.35637242\n",
      "Class name:  AIML . Score:  0.35540858\n",
      "Class name:  KXEN . Score:  0.35497415\n",
      "Class name:  GCN . Score:  0.3538786\n",
      "Class name:  Symbolic_based_AI . Score:  0.3538295\n",
      "Class name:  Denoising_VAE . Score:  0.35156992\n",
      "Class name:  AI_Programming_Language . Score:  0.35146472\n",
      "Class name:  Adversarial_Feature_Learning_&_Adversarially_Learned_Inference . Score:  0.3511786\n",
      "Class name:  GRU . Score:  0.34982675\n",
      "Class name:  Behavior_based_AI . Score:  0.34947464\n",
      "Class name:  Unsupervised_Person_Re-Identification . Score:  0.347511\n",
      "Class name:  Metaphor_and_simile . Score:  0.3452683\n",
      "Class name:  InfoGAN . Score:  0.34509495\n",
      "Class name:  Levenberg-Marquardt_backpropagation . Score:  0.34288958\n",
      "Class name:  Large-Scale_Person_Re-Identification . Score:  0.34274057\n",
      "Class name:  AI_Software_Library . Score:  0.34171593\n",
      "Class name:  Video-Based_Person_Re-Identification . Score:  0.33807504\n",
      "Class name:  Hough_Transform . Score:  0.33657655\n",
      "Class name:  Sinc_Function . Score:  0.3363628\n",
      "Class name:  GBM . Score:  0.33606076\n",
      "Class name:  Auxiliary_Classifier_GAN . Score:  0.33550978\n",
      "Class name:  YALE . Score:  0.3307766\n",
      "Class name:  Microsoft_CNTK . Score:  0.3288712\n",
      "Class name:  Boundary_Equilibrium_GAN . Score:  0.3281978\n",
      "Class name:  Grammatical_inference_by_trial-and-error . Score:  0.32800207\n",
      "Class name:  STATISTICA . Score:  0.32733834\n",
      "Class name:  Birch . Score:  0.32708895\n",
      "Class name:  ArSinH_Function . Score:  0.3262035\n",
      "Class name:  Few-Shot_Imitation_Learning . Score:  0.3252335\n",
      "Class name:  Circle_and_Ellipse_Detection . Score:  0.324476\n",
      "Class name:  Brill_tagger . Score:  0.32391924\n",
      "Class name:  L1_Loss . Score:  0.32366493\n",
      "Class name:  L1_Hinge_Error . Score:  0.32342255\n",
      "Class name:  Object_Labeling_and_Counting . Score:  0.32313034\n",
      "Class name:  Vanilla_VAE . Score:  0.32229742\n",
      "Class name:  AHE . Score:  0.32098764\n",
      "Class name:  Few-Shot_Learning . Score:  0.3206463\n",
      "Class name:  Depth_And_Camera_Motion . Score:  0.32001436\n",
      "Class name:  SBD . Score:  0.3174899\n",
      "Class name:  TanH__Function . Score:  0.3170459\n",
      "Class name:  CNN . Score:  0.3160484\n",
      "Class name:  Few-Shot_Camera_Adaptive_Color_Constancy . Score:  0.31552956\n",
      "Class name:  Few-Shot_Camera_Adaptive_Color_Constancy . Score:  0.31552956\n",
      "Class name:  Salt-And-Pepper_Noise_Removal . Score:  0.31537473\n",
      "Class name:  SQNL_Function . Score:  0.31503212\n",
      "Class name:  DiscoGAN . Score:  0.3149066\n",
      "Class name:  Co-Saliency_Detection . Score:  0.31455478\n",
      "Class name:  WP . Score:  0.31330648\n",
      "Class name:  AlexNet . Score:  0.31328577\n",
      "Class name:  Inverse_square_root_linear_unit_(ISRLU) . Score:  0.3124583\n",
      "Class name:  Knowledge_and_Reasoning . Score:  0.31204605\n",
      "Class name:  Mean_Absolute_Error_(MAE) . Score:  0.3104998\n",
      "Class name:  Design_and_Modelling . Score:  0.3091284\n",
      "Class name:  HMM-based_synthesis . Score:  0.3084127\n",
      "Class name:  Acoustic_Question_Answering . Score:  0.30756012\n",
      "Class name:  Corner_and_Interest_Point_Detection . Score:  0.30754468\n",
      "Class name:  Sigmoid_Linear_Unit_(SiLU) . Score:  0.30706686\n",
      "Class name:  Maxout_Function . Score:  0.30704084\n",
      "Class name:  DNA_sequencing . Score:  0.30693886\n",
      "Class name:  Deep_RNN . Score:  0.30591014\n",
      "Class name:  Skeletons_and_Thinning . Score:  0.30545497\n",
      "Class name:  Parameteric_rectified_linear_unit_(PReLU) . Score:  0.30445662\n",
      "Class name:  One-Shot_Learning . Score:  0.30416533\n",
      "Class name:  One-Class_SVM . Score:  0.30410695\n",
      "Class name:  SPSS . Score:  0.30394232\n",
      "Class name:  Synthetic-to-Real_Translation . Score:  0.303801\n",
      "Class name:  MOA . Score:  0.3036928\n",
      "Class name:  L2_Loss . Score:  0.30352578\n",
      "Class name:  RNN . Score:  0.3033066\n",
      "Class name:  Embodied_Question_Answering . Score:  0.3016576\n",
      "Class name:  Suffix-stripping_algorithms . Score:  0.30160257\n",
      "Class name:  Uncertain_knowledge_and_reasoning . Score:  0.30155236\n",
      "Class name:  Open_domain_question_answering . Score:  0.30097246\n",
      "Class name:  Invariants_and_Perspective . Score:  0.29962525\n",
      "Class name:  Bayesian_Ridge_Regression . Score:  0.29880047\n",
      "Class name:  PCA . Score:  0.2983418\n",
      "Class name:  Hidden_Markov_models . Score:  0.2975204\n",
      "Class name:  Visual_Question_Answering . Score:  0.29722485\n",
      "Class name:  Default_reasoning_and_the_qualification_problem . Score:  0.29696533\n",
      "Class name:  Binary_Cross-Entropy_(BCE) . Score:  0.29651394\n",
      "Class name:  mlpy . Score:  0.29590994\n",
      "Class name:  Video_Question_Answering . Score:  0.29485607\n",
      "Class name:  ResNet . Score:  0.29478976\n",
      "Class name:  Ruby . Score:  0.2945106\n",
      "Class name:  Facial_Recognition_and_Modelling . Score:  0.29401085\n",
      "Class name:  Photo_To_Caricature_Translation . Score:  0.29327402\n",
      "Class name:  Photo_To_Caricature_Translation . Score:  0.29327402\n",
      "Class name:  Leaky_rectified_linear_unit_(Leaky_ReLU) . Score:  0.29272366\n",
      "Class name:  Time-to-Adjacency_Analysis . Score:  0.2924348\n",
      "Class name:  ElliotSig_Function . Score:  0.29206634\n",
      "Class name:  Centura . Score:  0.29188356\n",
      "Class name:  Visual_and_Artistic . Score:  0.29169738\n",
      "Class name:  Indexing,_Retrieval_and_Unification . Score:  0.29156977\n",
      "Class name:  Few-Shot_Relation_Classification . Score:  0.29118386\n",
      "Class name:  Sammon_Mapping . Score:  0.29086325\n",
      "Class name:  RCNN . Score:  0.29008886\n",
      "Class name:  AutoML . Score:  0.28947702\n",
      "Class name:  Deep_Belief_Networks_(DBNs) . Score:  0.28909144\n",
      "Class name:  Binary_RBM_with_Contrastive_Divergence . Score:  0.28874278\n",
      "Class name:  Market_analysis_and_data_mining . Score:  0.28847408\n",
      "Class name:  Infrared_And_Visible_Image_Fusion . Score:  0.28743395\n",
      "Class name:  Naive_Bayes_Classifier . Score:  0.2867649\n",
      "Class name:  Binary_RBM_with_Persistent_Contrastive_Divergence . Score:  0.28603676\n",
      "Class name:  Zero-Shot_Learning . Score:  0.28554845\n",
      "Class name:  Partial_Least_Squares_Regression_(PLSR) . Score:  0.2850145\n",
      "Class name:  Provide_consultations_and_Companion_robots . Score:  0.28471747\n",
      "Class name:  Factual_Visual_Question_Answering . Score:  0.28457472\n",
      "Class name:  Bird_View_Synthesis . Score:  0.28455698\n",
      "Class name:  Cybernetics_and_brain_simulation . Score:  0.28448215\n",
      "Class name:  Question_Answering . Score:  0.28399104\n",
      "Class name:  Out-of-Distribution_Detection . Score:  0.28331795\n",
      "Class name:  Simultaneous_Localization_and_Mapping . Score:  0.28323284\n",
      "Class name:  Deep_feedforward_and_recurrent_neural_networks . Score:  0.28313744\n",
      "Class name:  Image_Transformations_and_Camera_Calibration . Score:  0.2830833\n",
      "Class name:  Gaussian_Error_Linear_Units_(GELU) . Score:  0.28189862\n",
      "Class name:  Soft_Clipping_Function . Score:  0.2816452\n",
      "Class name:  Hebbian_Learning . Score:  0.28156474\n",
      "Class name:  3D_Object_Super-Resolution . Score:  0.28152817\n",
      "Class name:  Text-To-Speech_Synthesis . Score:  0.28152278\n",
      "Class name:  Few-Shot_Image_Classification . Score:  0.28126782\n",
      "Class name:  Bidirectional_RNN . Score:  0.28116757\n",
      "Class name:  Conversational_Bot . Score:  0.28114277\n",
      "Class name:  Bootstrap_Aggregating_(Bagging) . Score:  0.28100517\n",
      "Class name:  SAS . Score:  0.28074777\n",
      "Class name:  Hidden_Markov_Models_based . Score:  0.28011918\n",
      "Class name:  Meta-Learning . Score:  0.28011775\n",
      "Class name:  Mini-Max_Algorithm . Score:  0.2793526\n",
      "Class name:  Text-to-Image_Generation . Score:  0.27919263\n",
      "Class name:  Text_Mining . Score:  0.27879745\n",
      "Class name:  Mean-Shift_Clustering . Score:  0.27780885\n",
      "Class name:  Conditional_random_fields_approach . Score:  0.2765106\n",
      "Class name:  Radial_basis_SVM . Score:  0.27632475\n",
      "Class name:  Spark-Mlib . Score:  0.2762059\n",
      "Class name:  DualGAN . Score:  0.2760968\n",
      "Class name:  Unsupervised_Image-To-Image_Translation . Score:  0.27604222\n",
      "Class name:  Local_Invariant_Feature_Detectors_and_Descriptors . Score:  0.2758078\n",
      "Class name:  OpenNN . Score:  0.27570572\n",
      "Class name:  Text-to-speech . Score:  0.27548\n",
      "Class name:  Document_To_Image_Conversion . Score:  0.27544528\n",
      "Class name:  SVM . Score:  0.27542394\n",
      "Class name:  Semi-supervised_Video_Object_Segmentation . Score:  0.27406943\n",
      "Class name:  Agents_and_Environment . Score:  0.2738915\n",
      "Class name:  Culture_and_Humanities . Score:  0.27386117\n",
      "Class name:  Adaptive_piecewise_linear_(APL) . Score:  0.27350682\n",
      "Class name:  Stacking . Score:  0.27242205\n",
      "Class name:  Nature_and_life_sciences . Score:  0.27227816\n",
      "Class name:  Inductive_Learning . Score:  0.27201182\n",
      "Class name:  Search_and_optimization . Score:  0.27181345\n",
      "Class name:  Arts_and_Entertainment . Score:  0.27160755\n",
      "Class name:  Randomized_leaky_rectified_linear_unit_(RReLU) . Score:  0.27121523\n",
      "Class name:  Mathematical_question_answering . Score:  0.27099097\n",
      "Class name:  Mixture_Discriminant_Analysis_(MDA) . Score:  0.27084246\n",
      "Class name:  Inverse_square_root_unit_(ISRU) . Score:  0.27060336\n",
      "Class name:  Migrating_Birds_Optimization . Score:  0.27058345\n",
      "Class name:  Exponential_linear_unit_(ELU) . Score:  0.2704006\n",
      "Class name:  Speaking_Style_Synthesis . Score:  0.27025107\n",
      "Class name:  SAP . Score:  0.27022874\n",
      "Class name:  Image_Dehazing . Score:  0.26955226\n",
      "Class name:  Marketing_and_Advertising . Score:  0.26952752\n",
      "Class name:  FAMa . Score:  0.26914412\n",
      "Class name:  Grammatical_inference_by_greedy_algorithms . Score:  0.2683131\n",
      "Class name:  Multimodal_Unsupervised_Image-To-Image_Translation . Score:  0.26819757\n",
      "Class name:  Tensorflow . Score:  0.26816538\n",
      "Class name:  VNLA . Score:  0.26803342\n",
      "Class name:  Generalized_Zero-Shot_Learning . Score:  0.26790702\n",
      "Class name:  Curved_Text_Detection . Score:  0.2676693\n",
      "Class name:  3D_Pose_Estimation . Score:  0.2667122\n",
      "Class name:  Drawing_Pictures . Score:  0.2667058\n",
      "Class name:  Zero-Shot_Action_Recognition . Score:  0.26639298\n",
      "Class name:  Mathematica . Score:  0.26573548\n",
      "Class name:  Single-Image_Blind_Deblurring . Score:  0.26564395\n",
      "Class name:  Image-to-Image_Translation . Score:  0.2654309\n",
      "Class name:  Low-Light_Image_Enhancement . Score:  0.26528373\n",
      "Class name:  Ant_colony_optimization . Score:  0.26506433\n",
      "Class name:  Mask-RCNN . Score:  0.26489976\n",
      "Class name:  Computer-aided_interpretation_of_medical_images . Score:  0.26401103\n",
      "Class name:  Text_Spotting . Score:  0.26391408\n",
      "Class name:  Low-Level_Vision . Score:  0.26386195\n",
      "Class name:  Weakly_Supervised_Object_Detection . Score:  0.26372916\n",
      "Class name:  Multi-agent_Reinforcement_Learning . Score:  0.2635416\n",
      "Class name:  Hyperspace_Analogue_to_Language . Score:  0.26340336\n",
      "Class name:  Bipolar_step_function . Score:  0.26322043\n",
      "Class name:  Blind_Image_Deblurring . Score:  0.26321015\n",
      "Class name:  Orange . Score:  0.26291525\n",
      "Class name:  Hill-Climbing_Search . Score:  0.26291272\n",
      "Class name:  Frame_Systems_and_Semantic_Networks . Score:  0.26267174\n",
      "Class name:  Binary_step_function . Score:  0.26213628\n",
      "Class name:  Mini_Batch_Gradient_Descent . Score:  0.26171008\n",
      "Class name:  Learning_from_Observations . Score:  0.26166403\n",
      "Class name:  ArcTan_Function . Score:  0.26142624\n",
      "Class name:  Weakly-Supervised_Semantic_Segmentation . Score:  0.26139954\n",
      "Class name:  Probabilistic_methods_for_uncertain_reasoning . Score:  0.26053745\n",
      "Class name:  Fine-Grained_Action_Detection . Score:  0.26031485\n",
      "Class name:  Manner_Of_Articulation_Detection . Score:  0.26026815\n",
      "Class name:  Multi-Task_Learning . Score:  0.26022303\n",
      "Class name:  Accord.net . Score:  0.25991243\n",
      "Class name:  Motion_Planning . Score:  0.25966173\n",
      "Class name:  Semi-Supervised_Learning . Score:  0.25958645\n",
      "Class name:  Robotic_Grasping . Score:  0.25956056\n",
      "Class name:  Stereo_from_Motion . Score:  0.2594058\n",
      "Class name:  Graph_Representation_Learning . Score:  0.25918916\n",
      "Class name:  Multi-class_Cross_Entropy_Loss . Score:  0.25915948\n",
      "Class name:  AZURE . Score:  0.25901133\n",
      "Class name:  Intent_Segmentation . Score:  0.2581528\n",
      "Class name:  Boltzmann_Machine . Score:  0.2581393\n",
      "Class name:  Self-Organizing_Maps . Score:  0.2580573\n",
      "Class name:  3D_Vision . Score:  0.2579613\n",
      "Class name:  Image/Document_Clustering . Score:  0.25774148\n",
      "Class name:  Bent_identity_Function . Score:  0.25753433\n",
      "Class name:  3D_Object_Recognition . Score:  0.25743082\n",
      "Class name:  Shogun . Score:  0.25730413\n",
      "Class name:  Geographic_information_system_(GIS) . Score:  0.25688982\n",
      "Class name:  High-throughput_imaging . Score:  0.25681517\n",
      "Class name:  Stata . Score:  0.25675574\n",
      "Class name:  Prolog . Score:  0.25675464\n",
      "Class name:  Sinusoid_Function . Score:  0.25637645\n",
      "Class name:  Text_Segmentaion . Score:  0.25597155\n",
      "Class name:  Multi-Oriented_Scene_Text_Detection . Score:  0.25580874\n",
      "Class name:  Principal_Component_Regression_(PCR) . Score:  0.2557304\n",
      "Class name:  Multiple_Object_Tracking . Score:  0.25551212\n",
      "Class name:  Converssational_Emotion_Recognition . Score:  0.25514913\n",
      "Class name:  Keypoint_Detection/Interest_Point_Detection . Score:  0.2549416\n",
      "Class name:  Multiple_Action_Detection . Score:  0.25490466\n",
      "Class name:  Keras . Score:  0.25489202\n",
      "Class name:  Markerless_Motion_Capture . Score:  0.25484914\n",
      "Class name:  No-Reference_Image_Quality_Assessment . Score:  0.25462994\n",
      "Class name:  Video_Super-Resolution . Score:  0.25455236\n",
      "Class name:  Verbal_Assistant . Score:  0.25452107\n",
      "Class name:  Sequential_Learning . Score:  0.25426176\n",
      "Class name:  Negative_Log_Likelihood . Score:  0.25415787\n",
      "Class name:  Person_Retrieval . Score:  0.2540732\n",
      "Class name:  Softstep_Function . Score:  0.2536466\n",
      "Class name:  Geoinformatics_(GIS) . Score:  0.25277936\n",
      "Class name:  LogitBoost . Score:  0.2525799\n",
      "Class name:  Softsign_Function . Score:  0.2525383\n",
      "Class name:  Random_Forest . Score:  0.25229046\n",
      "Class name:  Line_Detection . Score:  0.2522266\n",
      "Class name:  Weakly-Supervised_Object_Localization . Score:  0.25210783\n",
      "Class name:  Caffe . Score:  0.25203153\n",
      "Class name:  Gaussian_Function . Score:  0.251219\n",
      "Class name:  Hand-Gesture_Recognition . Score:  0.25121105\n",
      "Class name:  Adversarial_Variational_Bayes . Score:  0.25108942\n",
      "Class name:  AdaDelta . Score:  0.25104308\n",
      "Class name:  Expressive_Operator_Descriptions . Score:  0.25082225\n",
      "Class name:  Sigmoid_Function . Score:  0.25071517\n",
      "Class name:  LSTM . Score:  0.25047812\n",
      "Class name:  Low_Variance_Filter . Score:  0.25014865\n",
      "Class name:  Formant_synthesis . Score:  0.25014284\n",
      "Class name:  Knowledge_Graph_Embedding . Score:  0.24970198\n",
      "Class name:  Blind_Image_Quality_Assessment . Score:  0.24958974\n",
      "Class name:  3D_Shape_Analysis . Score:  0.24939638\n",
      "Class name:  Deep_learning_synthesis . Score:  0.24910645\n",
      "Class name:  Data_mining . Score:  0.24902608\n",
      "Class name:  Ridge_Regression . Score:  0.24899822\n",
      "Class name:  Multilayer_Feed-Forward_Networks . Score:  0.2489564\n",
      "Class name:  Bayesian_Inference . Score:  0.24860099\n",
      "Class name:  Conditional_Planning . Score:  0.24837248\n",
      "Class name:  Lisp . Score:  0.24823558\n",
      "Class name:  Jpeg_Compression_Artifact_Reduction . Score:  0.24807622\n",
      "Class name:  Fine-Grained_Image_Recognition . Score:  0.24728483\n",
      "Class name:  ATC . Score:  0.24717067\n",
      "Class name:  Skeleton_Based_Action_Recognition . Score:  0.24715821\n",
      "Class name:  Spoken_Language_Understanding . Score:  0.24697354\n",
      "Class name:  Robot_Task_Planning . Score:  0.24634357\n",
      "Class name:  Bayesian_Regression . Score:  0.24633342\n",
      "Class name:  Sign_Language_Translation . Score:  0.24626134\n",
      "Class name:  Object_Counting . Score:  0.24616721\n",
      "Class name:  Word_Segmentation . Score:  0.24611184\n",
      "Class name:  Multiview_Learning . Score:  0.24598338\n",
      "Class name:  Graph_Embedding . Score:  0.24595264\n",
      "Class name:  Line_segment_detection . Score:  0.24567941\n",
      "Class name:  OpenCV . Score:  0.24516429\n",
      "Class name:  Scaled_exponential_linear_unit_(SELU) . Score:  0.24486178\n",
      "Class name:  Imitation_Learning . Score:  0.24465153\n",
      "Class name:  Fake_Image_Detection . Score:  0.24461392\n",
      "Class name:  Fine_Grain_Image_Categorization . Score:  0.24446279\n",
      "Class name:  Conditional_Image_Generation . Score:  0.24404874\n",
      "Class name:  Multi-modal_image_segmentation . Score:  0.24387027\n",
      "Class name:  Travelling_Salesman_Problem . Score:  0.24375008\n",
      "Class name:  OCR . Score:  0.24373318\n",
      "Class name:  Voice_Conversion . Score:  0.24368933\n",
      "Class name:  Hypothesis_of_creative_patterns . Score:  0.24362205\n",
      "Class name:  neuro_imaging . Score:  0.24358663\n",
      "Class name:  Graddient_Boosting . Score:  0.24358088\n",
      "Class name:  GibbsNet . Score:  0.2433442\n",
      "Class name:  Mean_Square_Error_(MSE) . Score:  0.24328966\n",
      "Class name:  Observation_Completion . Score:  0.24309415\n",
      "Class name:  Fuzzy_Sets . Score:  0.24296366\n",
      "Class name:  Image_Super-Resolution . Score:  0.24256326\n",
      "Class name:  Natural_Language_Processing . Score:  0.24252407\n",
      "Class name:  Sentence_Segmentaion . Score:  0.24189119\n",
      "Class name:  Face_Verification . Score:  0.24170661\n",
      "Class name:  Temporal_Action_Proposal_Generation . Score:  0.24166965\n",
      "Class name:  Finance_and_Economics . Score:  0.241562\n",
      "Class name:  Unsupervised_Video_Object_Segmentation . Score:  0.241506\n",
      "Class name:  Image_Imputation . Score:  0.24148484\n",
      "Class name:  Optical_Character_Recognition_(OCR) . Score:  0.24144492\n",
      "Class name:  Multi-Goal_Reinforcement_Learning . Score:  0.24135618\n",
      "Class name:  Ordinary_Least_Squares_Regression . Score:  0.24111204\n",
      "Class name:  Named-Entity_Recognition . Score:  0.24104007\n",
      "Class name:  RANdom_SAmple_Consensus(RANSAC) . Score:  0.24097833\n",
      "Class name:  Motion_Segmentation . Score:  0.24080957\n",
      "Class name:  Statistical_learning . Score:  0.24080734\n",
      "Class name:  Handwritten_Digit_Recognition . Score:  0.24040255\n",
      "Class name:  Keyword_Spotting . Score:  0.24036716\n",
      "Class name:  Natural_Language_Understanding . Score:  0.23995619\n",
      "Class name:  Semi-Supervised_Image_Classification . Score:  0.2397364\n",
      "Class name:  3D_Part_Segmentation . Score:  0.23952606\n",
      "Class name:  Deformable_Object_Manipulation . Score:  0.23926093\n",
      "Class name:  Bayesian_Network . Score:  0.23908076\n",
      "Class name:  Binary_sigmoidal_function . Score:  0.23898517\n",
      "Class name:  Semantic_folding . Score:  0.23809399\n",
      "Class name:  Random_indexing . Score:  0.23689619\n",
      "Class name:  LeNet . Score:  0.23688056\n",
      "Class name:  Text-Independent_Speaker_Recognition . Score:  0.2367239\n",
      "Class name:  Cell_Imaging . Score:  0.23665695\n",
      "Class name:  Grammatical_inference_by_genetic_algorithms . Score:  0.23662502\n",
      "Class name:  Global_Valley_approach . Score:  0.23658046\n",
      "Class name:  Large_Vocabulary_Continuous_Speech_Recognition . Score:  0.23651098\n",
      "Class name:  Motion_Capture . Score:  0.2357522\n",
      "Class name:  EViews . Score:  0.23557386\n",
      "Class name:  Word_Embeddings . Score:  0.23508175\n",
      "Class name:  CPlusPlus . Score:  0.23506704\n",
      "Class name:  Backward_Feature_Elimination . Score:  0.23498829\n",
      "Class name:  Intermediate-Level_Vision . Score:  0.23481338\n",
      "Class name:  Activation_Functions . Score:  0.2346957\n",
      "Class name:  Sigmoidal_function . Score:  0.23468065\n",
      "Class name:  Bipolar_sigmoidal_function . Score:  0.23466972\n",
      "Class name:  Unsupervised_Anomaly_Detection_In_Sound . Score:  0.23460336\n",
      "Class name:  Kernel_SVM . Score:  0.234589\n",
      "Class name:  Diphone_synthesis . Score:  0.23450252\n",
      "Class name:  Super_Resolution . Score:  0.23450078\n",
      "Class name:  Forward_Feature_Selection . Score:  0.23449712\n",
      "Class name:  Intelligent_Water_Drops_Algorithm . Score:  0.2343912\n",
      "Class name:  scikit-learn . Score:  0.23432447\n",
      "Class name:  Sequence_to_Sequence_Speecg_Recognition . Score:  0.23405987\n",
      "Class name:  3D_Object_Classification . Score:  0.23387055\n",
      "Class name:  Soft_Computing . Score:  0.2338674\n",
      "Class name:  Multi-Label_Image_Retrieval . Score:  0.23378566\n",
      "Class name:  Video_Object_Segmentation . Score:  0.23378137\n",
      "Class name:  Passive_Learning . Score:  0.2337188\n",
      "Class name:  Pytorch . Score:  0.23371515\n",
      "Class name:  Multiple_Instance_Learning . Score:  0.23368263\n",
      "Class name:  Fine-Grained_Image_Classification . Score:  0.23348452\n",
      "Class name:  Density_Based_Spatial_Clustering . Score:  0.233072\n",
      "Class name:  Iterative_deepening_depth-first_search . Score:  0.23306604\n",
      "Class name:  Feature_Learning . Score:  0.23294333\n",
      "Class name:  Cross_Entropy_Loss . Score:  0.23282425\n",
      "Class name:  Human_Activity_Recognition . Score:  0.23239622\n",
      "Class name:  MLPACK . Score:  0.23237465\n",
      "Class name:  Rectified_linear_unit_(ReLU) . Score:  0.23231122\n",
      "Class name:  Non-Monotonic_Logic . Score:  0.23218851\n",
      "Class name:  Content-Based_Filtering . Score:  0.23214474\n",
      "Class name:  SoftPlus_Function . Score:  0.2316726\n",
      "Class name:  Supervised_Video_Summarization . Score:  0.23164025\n",
      "Class name:  Cell_Painting . Score:  0.23160337\n",
      "Class name:  Artificial_Bee_Colony_Optimization . Score:  0.23145524\n",
      "Class name:  3D_Shape_Representation . Score:  0.23137935\n",
      "Class name:  Depiction_Invariant_Object_Recognition . Score:  0.23081818\n",
      "Class name:  Methods_Based_on_Projections . Score:  0.23080114\n",
      "Class name:  Voice_Query_Recognition . Score:  0.23050754\n",
      "Class name:  Motion . Score:  0.23045112\n",
      "Class name:  Basic_Image_Filtering . Score:  0.23044676\n",
      "Class name:  Depth_Completion . Score:  0.2303898\n",
      "Class name:  Automated_Feature_Engineering . Score:  0.23037769\n",
      "Class name:  Machine_Translation . Score:  0.23006953\n",
      "Class name:  Sign_Language_Recognition . Score:  0.23004954\n",
      "Class name:  Scruffy . Score:  0.23001978\n",
      "Class name:  Squared_Error_Loss . Score:  0.22997057\n",
      "Class name:  Depth_Map_Super-Resolution . Score:  0.22969474\n",
      "Class name:  Visual_Object_Tracking . Score:  0.22928791\n",
      "Class name:  Computational_creativity . Score:  0.22914335\n",
      "Class name:  Gaze_Estimation . Score:  0.22901599\n",
      "Class name:  Machine_Learning . Score:  0.22899865\n",
      "Class name:  Supervised_Learning . Score:  0.22893748\n",
      "Class name:  3D_Semantic_Instance_Segmentation . Score:  0.22891648\n",
      "Class name:  RBM . Score:  0.22885358\n",
      "Class name:  Particle_swarm_optimization . Score:  0.22868975\n",
      "Class name:  Nesterov_accelerated_gradient . Score:  0.22862071\n",
      "Class name:  3D . Score:  0.22804219\n",
      "Class name:  Decision_Trees . Score:  0.22801134\n",
      "Class name:  Ad_Targeting . Score:  0.22789976\n",
      "Class name:  Interpretable_Machine_Learning . Score:  0.22779034\n",
      "Class name:  Local_Beam_Search . Score:  0.22771098\n",
      "Class name:  Support_Vector_Machine . Score:  0.22752853\n",
      "Class name:  Human-Object_Interaction_Detection . Score:  0.22752081\n",
      "Class name:  Motion_Estimation . Score:  0.22742656\n",
      "Class name:  Thermal_Infrared_Object_Tracking . Score:  0.2273261\n",
      "Class name:  Affix_stemmers . Score:  0.22702806\n",
      "Class name:  Hopfield_Network . Score:  0.2270037\n",
      "Class name:  Natural_Language_Generation . Score:  0.22699761\n",
      "Class name:  Deep_Learning . Score:  0.2269767\n",
      "Class name:  Defocus_Estimation . Score:  0.22651462\n",
      "Class name:  Continuous_Object_Recognition . Score:  0.22644173\n",
      "Class name:  Color_Image_Compression_Artifact_Reduction . Score:  0.22582403\n",
      "Class name:  Unsupervised_Representation_Learning . Score:  0.22580187\n",
      "Class name:  Metric_Learning . Score:  0.2257913\n",
      "Class name:  Word-based_morphology . Score:  0.22574489\n",
      "Class name:  Document_Embedding . Score:  0.22546561\n",
      "Class name:  Quadratic_Discriminant_Analysis_(QDA) . Score:  0.22542188\n",
      "Class name:  Size_Filtering . Score:  0.2252782\n",
      "Class name:  Image_Processing . Score:  0.22521032\n",
      "Class name:  Color_Constancy . Score:  0.22520609\n",
      "Class name:  Blind_Search . Score:  0.22520185\n",
      "Class name:  Cosine_Error . Score:  0.22485556\n",
      "Class name:  Ramp_function . Score:  0.22483243\n",
      "Class name:  3D_Instance_Segmentation . Score:  0.22481856\n",
      "Class name:  DLLearner . Score:  0.22459452\n",
      "Class name:  Active_Learning . Score:  0.22456002\n",
      "Class name:  Dynamic_time_warping . Score:  0.22452049\n",
      "Class name:  Latent_semantic_analysis . Score:  0.22430897\n",
      "Class name:  Visual_Crowd_Analysis . Score:  0.2241761\n",
      "Class name:  Face_Generation . Score:  0.223863\n",
      "Class name:  Art_Analysis . Score:  0.22385043\n",
      "Class name:  IBMMiner . Score:  0.22345869\n",
      "Class name:  Embedding_loss_functions . Score:  0.22340925\n",
      "Class name:  Auxiliary_Learning . Score:  0.22316173\n",
      "Class name:  Conversational_Speech_recognition . Score:  0.2230933\n",
      "Class name:  Morpheme-based_morphology . Score:  0.22307363\n",
      "Class name:  Digital_Image_Processing . Score:  0.22298074\n",
      "Class name:  Partially_Instantiated_Operators . Score:  0.22298042\n",
      "Class name:  Deep_Reinforcement_Learning . Score:  0.2229628\n",
      "Class name:  Distant_Speech_Recognition . Score:  0.22231077\n",
      "Class name:  Scene_Graph_Generation . Score:  0.22229525\n",
      "Class name:  Elliptic_Envelope . Score:  0.22229436\n",
      "Class name:  Facial_Expression_Recognition . Score:  0.22228356\n",
      "Class name:  Identity_function . Score:  0.22217624\n",
      "Class name:  Image_Manipulation_Detection . Score:  0.22209351\n",
      "Class name:  Decision-Tree_Regressor . Score:  0.22205885\n",
      "Class name:  Fuzzy_Systems . Score:  0.22196436\n",
      "Class name:  Digit_Recognition . Score:  0.22168593\n",
      "Class name:  Fuzzy_Logic . Score:  0.2216114\n",
      "Class name:  Predictive_Analytics_for_Marketing . Score:  0.22159407\n",
      "Class name:  Sketch_Recognition . Score:  0.221553\n",
      "Class name:  Group_Activity_Recognition . Score:  0.22151542\n",
      "Class name:  Proportional_Logic . Score:  0.22146466\n",
      "Class name:  Logistic_Function . Score:  0.22134861\n",
      "Class name:  Multi-class_Classification_Loss_Functions . Score:  0.22131109\n",
      "Class name:  High_Correlation_Filter . Score:  0.22127447\n",
      "Class name:  Representation_Learning . Score:  0.22123653\n",
      "Class name:  Sketch-Based_Image_Retrieval . Score:  0.22122906\n",
      "Class name:  Robot_Navigation . Score:  0.2210921\n",
      "Class name:  Isolated_Gestures_Recognition . Score:  0.22094283\n",
      "Class name:  Unit_selection_synthesis . Score:  0.22092569\n",
      "Class name:  Pure_Heuristic_Search . Score:  0.2207842\n",
      "Class name:  Grammar_Induction . Score:  0.22068492\n",
      "Class name:  Entropy_based_Thresholding . Score:  0.22055416\n",
      "Class name:  Joke_generation . Score:  0.2202046\n",
      "Class name:  Stochastic_Gradient_Descent_Regression . Score:  0.2201664\n",
      "Class name:  Image_Declipping . Score:  0.21991882\n",
      "Class name:  Optical_Flow_Estimation . Score:  0.2197944\n",
      "Class name:  Vision-Language_Navigation . Score:  0.21971911\n",
      "Class name:  Breadth-first_search . Score:  0.21952063\n",
      "Class name:  Collaborative_Filtering . Score:  0.21938014\n",
      "Class name:  Linear_Regression . Score:  0.21936324\n",
      "Class name:  Speaker_Identification . Score:  0.21934023\n",
      "Class name:  Face_Transfer . Score:  0.21921329\n",
      "Class name:  Mean_Bias_Error . Score:  0.21920186\n",
      "Class name:  Missing_Value_Ratio . Score:  0.21904556\n",
      "Class name:  Kullback_Leibler_Divergence_Loss . Score:  0.21867646\n",
      "Class name:  Expectation-Maximization . Score:  0.21834134\n",
      "Class name:  Unsupervised_Pretrained_Networks_(UPN) . Score:  0.21822247\n",
      "Class name:  Textual_Entailment_Recognition . Score:  0.21811004\n",
      "Class name:  Personal_Assistant . Score:  0.21783386\n",
      "Class name:  Hierarchical_Reinforcement_Learning . Score:  0.21758306\n",
      "Class name:  Brute_Force_Search . Score:  0.21758103\n",
      "Class name:  Sequitur . Score:  0.21742353\n",
      "Class name:  Bipolar_rectified_linear_unit_(BReLU) . Score:  0.21739142\n",
      "Class name:  Non-Optimization_based . Score:  0.21721767\n",
      "Class name:  Soft_Margin_Classifier . Score:  0.21719456\n",
      "Class name:  Face_Detection . Score:  0.2170247\n",
      "Class name:  Spatio-Temporal_Action_Localization . Score:  0.21701558\n",
      "Class name:  Audio-Visual_Video_Captioning . Score:  0.21690169\n",
      "Class name:  Speaker_Recognition . Score:  0.21678852\n",
      "Class name:  Sinewave_synthesis . Score:  0.21669497\n",
      "Class name:  3D_Reconstruction . Score:  0.21654934\n",
      "Class name:  MonteCarloMachineLearningLibrary . Score:  0.21654394\n",
      "Class name:  Speech_processing . Score:  0.21650311\n",
      "Class name:  Artificial_Fish_Swarm_Algorithm . Score:  0.21648645\n",
      "Class name:  Image_Clustering . Score:  0.21631555\n",
      "Class name:  Video_Quality_Assessment . Score:  0.216145\n",
      "Class name:  Logic_Based_Reasoning . Score:  0.21572414\n",
      "Class name:  Byte_pair_encoding . Score:  0.21567103\n",
      "Class name:  Spoken_Dialogue_Systems . Score:  0.21560934\n",
      "Class name:  Content-Based_Image_Retrieval . Score:  0.21557902\n",
      "Class name:  Face_Recognition . Score:  0.21557647\n",
      "Class name:  Big_Data . Score:  0.2155534\n",
      "Class name:  Object_Tracking . Score:  0.21529655\n",
      "Class name:  Orthogonal_Matching_Pursuit . Score:  0.21514812\n",
      "Class name:  Concatenation_synthesis . Score:  0.21494737\n",
      "Class name:  LibSVM . Score:  0.21484357\n",
      "Class name:  Accented_Speech_Recognition . Score:  0.21481025\n",
      "Class name:  Unsupervised_Video_Summarization . Score:  0.21416663\n",
      "Class name:  General_Ontology . Score:  0.21416572\n",
      "Class name:  Disease_prediction_and_diagnosis . Score:  0.2140368\n",
      "Class name:  neuro_biology . Score:  0.21401201\n",
      "Class name:  Contour_Detection . Score:  0.21389826\n",
      "Class name:  SoftExponential_Function . Score:  0.2137494\n",
      "Class name:  Least_Angle_Regression . Score:  0.21370655\n",
      "Class name:  First-order_Logic . Score:  0.21342582\n",
      "Class name:  Partial-Order_Planning . Score:  0.21321535\n",
      "Class name:  Content_Generation . Score:  0.21317165\n",
      "Class name:  Anomaly_Detection_In_Surveillance_Videos . Score:  0.21313852\n",
      "Class name:  Lossy-Compression_Artifact_Reduction . Score:  0.21289557\n",
      "Class name:  Machine_Vision . Score:  0.21255374\n",
      "Class name:  Dense_Video_Captioning . Score:  0.21253617\n",
      "Class name:  Audio-Visual_Speech_Recognition . Score:  0.21243636\n",
      "Class name:  warning_systems . Score:  0.21230748\n",
      "Class name:  Optical_Flow . Score:  0.21221554\n",
      "Class name:  Depth_Prediction_For_Outdoor_Scene . Score:  0.21220013\n",
      "Class name:  Stochastic_gradient_descent . Score:  0.21210343\n",
      "Class name:  Action_Detection . Score:  0.21169357\n",
      "Class name:  Transfer_Learning . Score:  0.211692\n",
      "Class name:  Transfer_Learning . Score:  0.211692\n",
      "Class name:  Transfer_Learning . Score:  0.211692\n",
      "Class name:  Acoustic_Modelling . Score:  0.2116268\n",
      "Class name:  Egocentric_Activity_Recognition . Score:  0.21153563\n",
      "Class name:  Gated_Recurrent_Unit . Score:  0.21140096\n",
      "Class name:  Distributed_Artificial_Intelligence . Score:  0.21132721\n",
      "Class name:  Missing_Markers_Reconstruction . Score:  0.21130833\n",
      "Class name:  Remote_Sensing . Score:  0.21107501\n",
      "Class name:  Active_Object_Localization . Score:  0.21105516\n",
      "Class name:  Variance_based_Thresholding . Score:  0.21102245\n",
      "Class name:  Java . Score:  0.2109996\n",
      "Class name:  Human_Computer_Interaction . Score:  0.21070336\n",
      "Class name:  Human_Computer_Interaction . Score:  0.21070336\n",
      "Class name:  Automatic_Relevance_Determination . Score:  0.21065776\n",
      "Class name:  Image_Rectification . Score:  0.21049729\n",
      "Class name:  Urban_Development . Score:  0.21017887\n",
      "Class name:  Tabu_Search_Algorithm . Score:  0.20992291\n",
      "Class name:  Binary_Shape_Analysis . Score:  0.20989178\n",
      "Class name:  Regression_Loss_Functions . Score:  0.20969655\n",
      "Class name:  Sentence_Embeddings . Score:  0.20953873\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class name:  Image_Denoising . Score:  0.2093506\n",
      "Class name:  Image_Compression . Score:  0.20934568\n",
      "Class name:  Image_Registration . Score:  0.20926993\n",
      "Class name:  Transfer_Reinforcement_Learning . Score:  0.20924543\n",
      "Class name:  Perl . Score:  0.20922303\n",
      "Class name:  Noisy_Speech_Recognition . Score:  0.20876482\n",
      "Class name:  Recurrent_Neural_Networks . Score:  0.20876373\n",
      "Class name:  Domain-specific_synthesis . Score:  0.20869516\n",
      "Class name:  Known_Environment . Score:  0.20858371\n",
      "Class name:  Known_Environment . Score:  0.20858371\n",
      "Class name:  Fluorescence_imaging . Score:  0.20855638\n",
      "Class name:  Density_Estimation . Score:  0.20829616\n",
      "Class name:  Image_Comprehension . Score:  0.20797206\n",
      "Class name:  Bacterial_Foraging_Optimization . Score:  0.20785809\n",
      "Class name:  Scene_Text_Recognition . Score:  0.20774224\n",
      "Class name:  Image_Matting . Score:  0.20753083\n",
      "Class name:  Video_Saliency_Detection . Score:  0.20731929\n",
      "Class name:  Unsupervised_Learning . Score:  0.2072138\n",
      "Class name:  Speech_Verification . Score:  0.20695654\n",
      "Class name:  Deep_Attention . Score:  0.20679931\n",
      "Class name:  Local_Color_Enhancement . Score:  0.20679224\n",
      "Class name:  Speech_denoising . Score:  0.20678943\n",
      "Class name:  Generative_Adversarial_Networks . Score:  0.20674643\n",
      "Class name:  Binary_Classification_Loss_Functions . Score:  0.20663148\n",
      "Class name:  Novel_View_Synthesis . Score:  0.20660985\n",
      "Class name:  Adaptive_Thresholding . Score:  0.20634611\n",
      "Class name:  DBM . Score:  0.2060977\n",
      "Class name:  Image_Quality_Assessment . Score:  0.2060672\n",
      "Class name:  Description_Logic . Score:  0.2058853\n",
      "Class name:  Feature_Engineering . Score:  0.20575735\n",
      "Class name:  Scene_Text_Detection . Score:  0.20547612\n",
      "Class name:  Boundary_Tracking . Score:  0.20532341\n",
      "Class name:  Products_recognition . Score:  0.20522575\n",
      "Class name:  Subsumption_Architecture . Score:  0.20458229\n",
      "Class name:  Material_Recognition . Score:  0.20457649\n",
      "Class name:  Recommendation_Systems . Score:  0.20448577\n",
      "Class name:  Recommendation_Systems . Score:  0.20448577\n",
      "Class name:  Computer_Vision . Score:  0.20446196\n",
      "Class name:  Articulatory_synthesis . Score:  0.20426294\n",
      "Class name:  Visual_Speech_recognition . Score:  0.20418118\n",
      "Class name:  Scene_Understanding . Score:  0.20417538\n",
      "Class name:  Loss_Function . Score:  0.20388168\n",
      "Class name:  Pose_Estimation . Score:  0.20381515\n",
      "Class name:  Embodied_intelligence . Score:  0.20376618\n",
      "Class name:  Interactive_Segmentation . Score:  0.20367749\n",
      "Class name:  Sentiment_Analysis . Score:  0.20361607\n",
      "Class name:  Topic_Segmentation . Score:  0.20358442\n",
      "Class name:  Unknown_Environment . Score:  0.20348224\n",
      "Class name:  Unknown_Environment . Score:  0.20348224\n",
      "Class name:  Distributional_learning . Score:  0.20347025\n",
      "Class name:  Data_Augmentation . Score:  0.20338319\n",
      "Class name:  Optical_Character_Recognition . Score:  0.20332865\n",
      "Class name:  Session-Based_Recommendations . Score:  0.20332558\n",
      "Class name:  Visual_arts . Score:  0.20313266\n",
      "Class name:  Spectral_Clustering . Score:  0.203103\n",
      "Class name:  Simulated_Annealing . Score:  0.2030615\n",
      "Class name:  Elastic_net . Score:  0.20303814\n",
      "Class name:  Elastic_net . Score:  0.20303814\n",
      "Class name:  Projection_Pursuit . Score:  0.2027212\n",
      "Class name:  Gradient_Descent . Score:  0.20257856\n",
      "Class name:  Bots . Score:  0.20236224\n",
      "Class name:  Dense_Pixel_Correspondence_Estimation . Score:  0.20222038\n",
      "Class name:  Video_Summarization . Score:  0.20221278\n",
      "Class name:  Hierarchical_Clustering . Score:  0.2020191\n",
      "Class name:  Multi-Layer_Perceptron . Score:  0.20198132\n",
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      "Class name:  Object_Recognition . Score:  0.20172878\n",
      "Class name:  Neural_Architecture_Search . Score:  0.20168538\n",
      "Class name:  Document_Image_Classification . Score:  0.20148446\n",
      "Class name:  Image_Generation . Score:  0.20146789\n",
      "Class name:  KNIME . Score:  0.20129293\n",
      "Class name:  genes_prediction . Score:  0.20119423\n",
      "Class name:  Planning . Score:  0.20118086\n",
      "Class name:  Image_Matching . Score:  0.20114665\n",
      "Class name:  Story_generation . Score:  0.20105536\n",
      "Class name:  Gradient-Boost_Regressor . Score:  0.20104311\n",
      "Class name:  Distributional_Reinforcement_Learning . Score:  0.20082094\n",
      "Class name:  Visual_Sentiment_Prediction . Score:  0.20062926\n",
      "Class name:  Softmax_Function . Score:  0.20062011\n",
      "Class name:  Unsupervised_Object_Localization . Score:  0.20061684\n",
      "Class name:  H2O . Score:  0.20047581\n",
      "Class name:  Scene_Flow_Estimation . Score:  0.20019805\n",
      "Class name:  language-based_assistance . Score:  0.2000781\n",
      "Class name:  Deblurring . Score:  0.19992554\n",
      "Class name:  Depth-first_search . Score:  0.19984129\n",
      "Class name:  Car_Racing . Score:  0.19973442\n",
      "Class name:  Video_Captioning . Score:  0.19969282\n",
      "Class name:  Generative_Adversarial_Parallelization . Score:  0.19963853\n",
      "Class name:  Object_Detection . Score:  0.19949135\n",
      "Class name:  Cell_biology . Score:  0.19922967\n",
      "Class name:  Visual_Navigation . Score:  0.19909537\n",
      "Class name:  Reinforcement_Learning . Score:  0.19893593\n",
      "Class name:  Logical_reasoning . Score:  0.19877478\n",
      "Class name:  Nearest_Neighbor . Score:  0.19872229\n",
      "Class name:  Medication_management . Score:  0.19837253\n",
      "Class name:  Face_Reconstruction . Score:  0.197986\n",
      "Class name:  Speech_Synthesis . Score:  0.19780049\n",
      "Class name:  Lasso_Regression . Score:  0.1974684\n",
      "Class name:  Recursive_Neural_Networks . Score:  0.19738786\n",
      "Class name:  Constrained_Diffeomorphic_Image_Registration . Score:  0.19732401\n",
      "Class name:  Speech_Emotion_Recognition . Score:  0.19722949\n",
      "Class name:  Image_categorization . Score:  0.19704354\n",
      "Class name:  Greedy_Search . Score:  0.19700398\n",
      "Class name:  Music_Genre_Transfer . Score:  0.19681823\n",
      "Class name:  Clustering_Methods . Score:  0.19680229\n",
      "Class name:  Dimensionality_Reduction_methods . Score:  0.19654647\n",
      "Class name:  Deep_Adversarial_Networks . Score:  0.19615808\n",
      "Class name:  Image_Captioning . Score:  0.1959325\n",
      "Class name:  Second_Order_Optimization_Algorithms . Score:  0.19584677\n",
      "Class name:  Automatic_Summarization . Score:  0.19578858\n",
      "Class name:  Uniform_cost_search . Score:  0.19576651\n",
      "Class name:  imaging . Score:  0.1955356\n",
      "Class name:  MATLAB . Score:  0.1949604\n",
      "Class name:  Policy_Gradient_Methods . Score:  0.194786\n",
      "Class name:  Hyperspectral_Image_Segmentation . Score:  0.19468513\n",
      "Class name:  Salient_Object_Detection . Score:  0.19453722\n",
      "Class name:  Feature_Importance . Score:  0.19451421\n",
      "Class name:  Language-Based_Temporal_Localization . Score:  0.19431396\n",
      "Class name:  Activity_Recognition . Score:  0.19428092\n",
      "Class name:  General_Logical_Descriptions . Score:  0.19407043\n",
      "Class name:  Robust_Speech_Recognition . Score:  0.19402446\n",
      "Class name:  Sparse_Learning . Score:  0.19385616\n",
      "Class name:  General_Intelligence . Score:  0.19383435\n",
      "Class name:  Image_Recognition . Score:  0.19353239\n",
      "Class name:  Gradient_Boost . Score:  0.19352297\n",
      "Class name:  Face_Alignment . Score:  0.19319406\n",
      "Class name:  Context_based_Emotion_Recognition . Score:  0.19317374\n",
      "Class name:  Monocular_Depth_Estimation . Score:  0.19312479\n",
      "Class name:  Portfolio_management . Score:  0.19298823\n",
      "Class name:  Stemming . Score:  0.19281681\n",
      "Class name:  Encog . Score:  0.19243409\n",
      "Class name:  Clustering . Score:  0.19242442\n",
      "Class name:  Terminology_Extraction . Score:  0.19241622\n",
      "Class name:  Utility_Theory . Score:  0.19238603\n",
      "Class name:  Image_Deconvolution . Score:  0.19237967\n",
      "Class name:  Network_Embedding . Score:  0.19232993\n",
      "Class name:  Lexeme-based_morphology . Score:  0.19226111\n",
      "Class name:  Effector . Score:  0.19221927\n",
      "Class name:  RapidMiner . Score:  0.1922127\n",
      "Class name:  RCASE . Score:  0.19202726\n",
      "Class name:  Action_Localization . Score:  0.19181712\n",
      "Class name:  Octave . Score:  0.19170088\n",
      "Class name:  Robustness_regression . Score:  0.19154464\n",
      "Class name:  Design_treatment_plans . Score:  0.19150908\n",
      "Class name:  Neural-network_based . Score:  0.19147496\n",
      "Class name:  First_Order_Optimization_Algorithms . Score:  0.19110104\n",
      "Class name:  Relationship_Extraction . Score:  0.19096196\n",
      "Class name:  Texture_Synthesis . Score:  0.1908913\n",
      "Class name:  Emotion_Recognition . Score:  0.19075602\n",
      "Class name:  Video_games . Score:  0.19073492\n",
      "Class name:  Speech-Segmentation . Score:  0.19068286\n",
      "Class name:  Classification_Loss_Functions . Score:  0.19067791\n",
      "Class name:  Statistical_Methods . Score:  0.19062373\n",
      "Class name:  Feature_Selection . Score:  0.1906112\n",
      "Class name:  Unbalanced_Segmentation . Score:  0.1905314\n",
      "Class name:  Image-Based_Localization . Score:  0.19048592\n",
      "Class name:  Python . Score:  0.19026121\n",
      "Class name:  disaster_response . Score:  0.19007125\n",
      "Class name:  Decision_Stump . Score:  0.19006774\n",
      "Class name:  Intelligent_Agent . Score:  0.18988974\n",
      "Class name:  Adagrad . Score:  0.18965778\n",
      "Class name:  Drug_creation . Score:  0.1894387\n",
      "Class name:  Fraud_detection . Score:  0.18906371\n",
      "Class name:  Unsupervised_Domain_Adaptation . Score:  0.18865015\n",
      "Class name:  Multimodal_Emotion_Recognition . Score:  0.1886368\n",
      "Class name:  BrownBoost . Score:  0.18824974\n",
      "Class name:  Event-based_vision . Score:  0.18818772\n",
      "Class name:  Molecular_Docking . Score:  0.18813404\n",
      "Class name:  Video_Classification . Score:  0.18789153\n",
      "Class name:  Artificial_Neural_Networks . Score:  0.18785255\n",
      "Class name:  Action_Classification . Score:  0.18784499\n",
      "Class name:  Swarm_Intelligence . Score:  0.18748972\n",
      "Class name:  Radial-basis_network . Score:  0.18741876\n",
      "Class name:  Data_Science . Score:  0.18741067\n",
      "Class name:  Word-Sense_Disambiguation . Score:  0.18734188\n",
      "Class name:  Sensor_Modeling . Score:  0.18731667\n",
      "Class name:  Molecular_biology . Score:  0.18714376\n",
      "Class name:  Wide_Baseline_Matching . Score:  0.18701541\n",
      "Class name:  Evolutionary_Computation . Score:  0.18697113\n",
      "Class name:  Gesture_Recognition . Score:  0.18677557\n",
      "Class name:  Modal_Logic . Score:  0.18672386\n",
      "Class name:  Computational_intelligence . Score:  0.1867223\n",
      "Class name:  Camera_Localization . Score:  0.18660398\n",
      "Class name:  Yooreeka . Score:  0.18656163\n",
      "Class name:  Knowledge_Engineering . Score:  0.18655042\n",
      "Class name:  Dynamic_Belief_Networks . Score:  0.18624933\n",
      "Class name:  Coreference_Resolution . Score:  0.18592267\n",
      "Class name:  Image_Enhancement . Score:  0.18591492\n",
      "Class name:  Image_Similarity_Search . Score:  0.18563412\n",
      "Class name:  Clinical_training . Score:  0.18525109\n",
      "Class name:  Forgery . Score:  0.18519145\n",
      "Class name:  Denoising . Score:  0.18516125\n",
      "Class name:  Predictive_customer_service . Score:  0.18507963\n",
      "Class name:  LibLinear . Score:  0.18505688\n",
      "Class name:  Local_Outlier_Factor . Score:  0.18488479\n",
      "Class name:  Local_Search . Score:  0.18472475\n",
      "Class name:  Semantic_Segmentation . Score:  0.18470329\n",
      "Class name:  UMAP . Score:  0.18460587\n",
      "Class name:  Quadratic_Loss . Score:  0.18437597\n",
      "Class name:  Image_Stylization . Score:  0.18419722\n",
      "Class name:  Partial_Domain_Adaptation . Score:  0.18399292\n",
      "Class name:  Combinatorial_Optimization . Score:  0.18391746\n",
      "Class name:  Tribox_method . Score:  0.18374036\n",
      "Class name:  Boundary_Pattern_Analysis . Score:  0.18350087\n",
      "Class name:  Passive_Aggressive_Algorithms . Score:  0.18335748\n",
      "Class name:  Group_Anomaly_Detection . Score:  0.18330397\n",
      "Class name:  Logic_based . Score:  0.1830743\n",
      "Class name:  Dynamic_Decision_Networks . Score:  0.18287782\n",
      "Class name:  Thresholding . Score:  0.18281777\n",
      "Class name:  Lexical_recognition . Score:  0.18259911\n",
      "Class name:  Iterative_deepening_A*_Search . Score:  0.18242317\n",
      "Class name:  Adversarial_Autoencoder . Score:  0.18241617\n",
      "Class name:  Factor_Analysis . Score:  0.18224457\n",
      "Class name:  Object_Reconstruction . Score:  0.18199404\n",
      "Class name:  Absolute_Error_Loss . Score:  0.18199039\n",
      "Class name:  Dimensionality_Reduction . Score:  0.18166673\n",
      "Class name:  Edge_Detection . Score:  0.1815613\n",
      "Class name:  Cognitive_simulation . Score:  0.1815544\n",
      "Class name:  Probability . Score:  0.18148641\n",
      "Class name:  Style_Transfer . Score:  0.18136694\n",
      "Class name:  Dynamic_Texture_Recognition . Score:  0.18123114\n",
      "Class name:  Object_Localization . Score:  0.18107772\n",
      "Class name:  Logistic_Regression . Score:  0.18107668\n",
      "Class name:  Image_Stitching . Score:  0.18107115\n",
      "Class name:  Continuous_Control . Score:  0.18106107\n",
      "Class name:  Neologism . Score:  0.18099096\n",
      "Class name:  Boundary_Detection . Score:  0.18077472\n",
      "Class name:  Camera_Relocalization . Score:  0.18073454\n",
      "Class name:  Pedestrian_Detection . Score:  0.18038961\n",
      "Class name:  Shape_recognition . Score:  0.18035229\n",
      "Class name:  Parsing . Score:  0.18025249\n",
      "Class name:  Image_Retrieval . Score:  0.18019019\n",
      "Class name:  Algorithmic_trading . Score:  0.18018547\n",
      "Class name:  Entity_Embeddings . Score:  0.17961022\n",
      "Class name:  Image_Restoration . Score:  0.17955\n",
      "Class name:  Speech_Recognition . Score:  0.1795022\n",
      "Class name:  Speech_Recognition . Score:  0.1795022\n",
      "Class name:  Belief-network . Score:  0.17935105\n",
      "Class name:  Morphological_Segmentation . Score:  0.17923896\n",
      "Class name:  Demosaicking . Score:  0.17909285\n",
      "Class name:  Spectral_Estimation . Score:  0.17883243\n",
      "Class name:  Flexible_Discriminant_Analysis_(FDA) . Score:  0.17865425\n",
      "Class name:  Life_sciences . Score:  0.1786507\n",
      "Class name:  Video_Prediction . Score:  0.17806077\n",
      "Class name:  structure_regularization_method . Score:  0.17780913\n",
      "Class name:  Speech_Enhancement . Score:  0.17767681\n",
      "Class name:  Income . Score:  0.17754178\n",
      "Class name:  Visual_Localization . Score:  0.17748019\n",
      "Class name:  Materials_Screening . Score:  0.17708366\n",
      "Class name:  Applications . Score:  0.17698292\n",
      "Class name:  LIONsolver . Score:  0.17685547\n",
      "Class name:  Knowledge_based . Score:  0.17677787\n",
      "Class name:  Neural_Networks . Score:  0.17676018\n",
      "Class name:  Data_Analytics . Score:  0.17633304\n",
      "Class name:  Model_Compression . Score:  0.17593908\n",
      "Class name:  Scene_Parsing . Score:  0.1757088\n",
      "Class name:  Dynamic_Pricing . Score:  0.17551728\n",
      "Class name:  Satellite_Imaging/Satellite_Imagery . Score:  0.17543477\n",
      "Class name:  Stereo_Depth_Estimation . Score:  0.17530699\n",
      "Class name:  Recommendation_Engine . Score:  0.17516692\n",
      "Class name:  Cyber_Security . Score:  0.1749898\n",
      "Class name:  Hyperspectral_Image_Classification . Score:  0.17484322\n",
      "Class name:  Audio-Visual_Synchronization . Score:  0.17438357\n",
      "Class name:  Ensemble_Learning . Score:  0.17429286\n",
      "Class name:  Intrusion_Detection . Score:  0.1742443\n",
      "Class name:  Boosting . Score:  0.1740348\n",
      "Class name:  Depth_Estimation . Score:  0.17400987\n",
      "Class name:  Convolutional_Neural_Networks . Score:  0.17393391\n",
      "Class name:  Accuracy_Metrics . Score:  0.17388612\n",
      "Class name:  Image_Cropping . Score:  0.17371176\n",
      "Class name:  Predictive_Domain_Adaptation . Score:  0.17367566\n",
      "Class name:  Steering_Control . Score:  0.17338534\n",
      "Class name:  Intrinsic_Image_Decomposition . Score:  0.1732168\n",
      "Class name:  Object_Classification . Score:  0.17308243\n",
      "Class name:  Material_Classification . Score:  0.17262818\n",
      "Class name:  Video_Inpainting . Score:  0.17225921\n",
      "Class name:  Image_Classification . Score:  0.17202742\n",
      "Class name:  Ontology . Score:  0.17192717\n",
      "Class name:  Survey . Score:  0.17176747\n",
      "Class name:  Fraudalent_ads . Score:  0.17170572\n",
      "Class name:  Image_Reconstruction . Score:  0.17135985\n",
      "Class name:  Spectrum_Cartography . Score:  0.1709106\n",
      "Class name:  Viewpoint_Estimation . Score:  0.17053458\n",
      "Class name:  Trajectory_Prediction . Score:  0.17044029\n",
      "Class name:  Lipreading . Score:  0.17030767\n",
      "Class name:  Speech_Separation . Score:  0.17030357\n",
      "Class name:  Construction . Score:  0.17026117\n",
      "Class name:  quality . Score:  0.16930754\n",
      "Class name:  Terrain_Mapping . Score:  0.16894375\n",
      "Class name:  Tasks . Score:  0.16885664\n",
      "Class name:  Hinge_Loss . Score:  0.16862525\n",
      "Class name:  Domain_Adaptation . Score:  0.16838755\n",
      "Class name:  Manufacturing . Score:  0.16825274\n",
      "Class name:  ELKI . Score:  0.16818985\n",
      "Class name:  Best_First_Search . Score:  0.16709349\n",
      "Class name:  Pharmaceutical . Score:  0.16640638\n",
      "Class name:  Artificial_Intelligence . Score:  0.16598067\n",
      "Class name:  Marketing . Score:  0.16587096\n",
      "Class name:  Image_Steganography . Score:  0.1658423\n",
      "Class name:  Discourse . Score:  0.16553462\n",
      "Class name:  Scene_Segmentation . Score:  0.16524313\n",
      "Class name:  Stochastic_Optimization . Score:  0.16512676\n",
      "Class name:  Saliency_Detection . Score:  0.16477518\n",
      "Class name:  Arts . Score:  0.1647367\n",
      "Class name:  Variational_Autoencoder . Score:  0.16449897\n",
      "Class name:  Optical_imagery . Score:  0.16417335\n",
      "Class name:  Heuristic_Search . Score:  0.16381948\n",
      "Class name:  Mixture_Model . Score:  0.16372442\n",
      "Class name:  Matching_algorithms . Score:  0.1631243\n",
      "Class name:  Visual_Odometry . Score:  0.16264404\n",
      "Class name:  Unsupervised_Anomaly_Detection . Score:  0.16249129\n",
      "Class name:  Outlier_Detection . Score:  0.16206102\n",
      "Class name:  Scene_Recognition . Score:  0.16182707\n",
      "Class name:  Temporal_Localization . Score:  0.16167311\n",
      "Class name:  Stereo_Matching . Score:  0.16128637\n",
      "Class name:  Personal_finance . Score:  0.16127206\n",
      "Class name:  Bidirectional_Search . Score:  0.16110997\n",
      "Class name:  Scene-Aware_Dialogue . Score:  0.16105562\n",
      "Class name:  Medicine . Score:  0.16088685\n",
      "Class name:  Model_Selection . Score:  0.16088395\n",
      "Class name:  Regression . Score:  0.16061468\n",
      "Class name:  Hierarchical_Decomposition . Score:  0.16049416\n",
      "Class name:  Marketing_Forecasting . Score:  0.16012853\n",
      "Class name:  problem_solving . Score:  0.15947466\n",
      "Class name:  problem_solving . Score:  0.15947466\n",
      "Class name:  Musical . Score:  0.15863389\n",
      "Class name:  Network_Pruning . Score:  0.15863064\n",
      "Class name:  aerial_imagery . Score:  0.15834044\n",
      "Class name:  Flower_Pollination_Optimization . Score:  0.1577606\n",
      "Class name:  Decision_Theory . Score:  0.15766029\n",
      "Class name:  Automobiles . Score:  0.15715064\n",
      "Class name:  Gravitational_Search_Algorithm . Score:  0.15676878\n",
      "Class name:  image_outpainting . Score:  0.15668319\n",
      "Class name:  Affinity_Propogation . Score:  0.1565869\n",
      "Class name:  Hyperspectral_Unmixing . Score:  0.15538934\n",
      "Class name:  Affective_Computing . Score:  0.15528052\n",
      "Class name:  Information_Theory . Score:  0.1552252\n",
      "Class name:  Instance_Segmentation . Score:  0.15521073\n",
      "Class name:  Animation . Score:  0.1551595\n",
      "Class name:  Sociology . Score:  0.1550217\n",
      "Class name:  Homography_Estimation . Score:  0.15475768\n",
      "Class name:  Kernel_Methods . Score:  0.15470158\n",
      "Class name:  Social_Intelligence . Score:  0.15449837\n",
      "Class name:  Analogy . Score:  0.15427433\n",
      "Class name:  Lexical_Semantics . Score:  0.15413593\n",
      "Class name:  Pattern_recognition . Score:  0.1538226\n",
      "Class name:  Underwriting . Score:  0.15360889\n",
      "Class name:  Style_Generalization . Score:  0.15348208\n",
      "Class name:  Digital_Preservation . Score:  0.15340747\n",
      "Class name:  Intelligent_Surveillance . Score:  0.15319625\n",
      "Class name:  Thematics . Score:  0.15302531\n",
      "Class name:  Perception . Score:  0.15284923\n",
      "Class name:  Automotive . Score:  0.15265788\n",
      "Class name:  microscopy . Score:  0.15205835\n",
      "Class name:  Photography . Score:  0.15188727\n",
      "Class name:  Programmatic_Advertising . Score:  0.15178795\n",
      "Class name:  Performance_Metrics . Score:  0.15116669\n",
      "Class name:  Autoencoder . Score:  0.1510067\n",
      "Class name:  forecast_system . Score:  0.15100516\n",
      "Class name:  Mathematical_Morphology . Score:  0.15099533\n",
      "Class name:  Computer_Security . Score:  0.15091163\n",
      "Class name:  Pattern_Matching . Score:  0.15059423\n",
      "Class name:  Image_Inpainting . Score:  0.15052971\n",
      "Class name:  Classification_Methods . Score:  0.15049942\n",
      "Class name:  News . Score:  0.15045553\n",
      "Class name:  Hyperparameter_Optimization . Score:  0.14963977\n",
      "Class name:  culture . Score:  0.14935957\n",
      "Class name:  Texture_Classification . Score:  0.14934553\n",
      "Class name:  Assistance . Score:  0.1493098\n",
      "Class name:  Music . Score:  0.14925042\n",
      "Class name:  Poetry . Score:  0.14889242\n",
      "Class name:  Stochastic_algorithms . Score:  0.14887412\n",
      "Class name:  Distributional_Semantics . Score:  0.1486655\n",
      "Class name:  Disaster_Management . Score:  0.14866528\n",
      "Class name:  Facial_Inpainting . Score:  0.14814728\n",
      "Class name:  Evolutionary_algorithms . Score:  0.14792493\n",
      "Class name:  Keypoint_Localization . Score:  0.14760429\n",
      "Class name:  Margin_Classifier . Score:  0.14733714\n",
      "Class name:  Domain_Generalization . Score:  0.146925\n",
      "Class name:  Game_Theory . Score:  0.14666465\n",
      "Class name:  Crime . Score:  0.14624262\n",
      "Class name:  Speech . Score:  0.14570841\n",
      "Class name:  Genetic_Algorithm_Optimization . Score:  0.14412294\n",
      "Class name:  AdaBoost . Score:  0.14391863\n",
      "Class name:  Audit . Score:  0.14363553\n",
      "Class name:  VGGNet . Score:  0.14345655\n",
      "Class name:  Biology . Score:  0.14324676\n",
      "Class name:  Sensor . Score:  0.14224823\n",
      "Class name:  Search_Algorithms . Score:  0.14174072\n",
      "Class name:  Autoencoders . Score:  0.14151931\n",
      "Class name:  Literature . Score:  0.14086166\n",
      "Class name:  radiations . Score:  0.14071506\n",
      "Class name:  Banking . Score:  0.13962732\n",
      "Class name:  Digital_media . Score:  0.13861851\n",
      "Class name:  environmental . Score:  0.13779625\n",
      "Class name:  Lemmatisation_algorithms . Score:  0.13724275\n",
      "Class name:  Architecture . Score:  0.136676\n",
      "Class name:  Trade . Score:  0.13646519\n",
      "Class name:  Instance_Search . Score:  0.13645078\n",
      "Class name:  Transportation . Score:  0.13636404\n",
      "Class name:  Autonomous_Vehicles . Score:  0.13577223\n",
      "Class name:  Autonomous_Vehicles . Score:  0.13577223\n",
      "Class name:  Military . Score:  0.13567929\n",
      "Class name:  Multiclass_Classification . Score:  0.13547194\n",
      "Class name:  Saliency_Prediction . Score:  0.13526449\n",
      "Class name:  Agglomerative . Score:  0.13513714\n",
      "Class name:  Scene_Classification . Score:  0.1349141\n",
      "Class name:  Disparity_Estimation . Score:  0.13435262\n",
      "Class name:  Anomaly_Detection . Score:  0.13424838\n",
      "Class name:  XGBoost . Score:  0.13402945\n",
      "Class name:  XGBoost . Score:  0.13402945\n",
      "Class name:  Revenue_Forecast . Score:  0.13376407\n",
      "Class name:  Optimization_Algorithms . Score:  0.13371688\n",
      "Class name:  Traffic . Score:  0.1336084\n",
      "Class name:  Phonotactic_cues . Score:  0.1336011\n",
      "Class name:  Politics . Score:  0.13242812\n",
      "Class name:  Metamerism . Score:  0.13189963\n",
      "Class name:  Syntax . Score:  0.13183564\n",
      "Class name:  multimedia . Score:  0.1317194\n",
      "Class name:  Genetic_Algorithms . Score:  0.13144374\n",
      "Class name:  Movies . Score:  0.1313135\n",
      "Class name:  CatBoost . Score:  0.13068974\n",
      "Class name:  CatBoost . Score:  0.13068974\n",
      "Class name:  Transport . Score:  0.13059494\n",
      "Class name:  Games . Score:  0.12997265\n",
      "Class name:  Health_Care . Score:  0.12971431\n",
      "Class name:  Census . Score:  0.12968725\n",
      "Class name:  Diabetes . Score:  0.12968035\n",
      "Class name:  Sports . Score:  0.12964903\n",
      "Class name:  Texture . Score:  0.12940907\n",
      "Class name:  Anamoly_Detection . Score:  0.12880145\n",
      "Class name:  OracleDataMining . Score:  0.12877315\n",
      "Class name:  Linguistics . Score:  0.12839942\n",
      "Class name:  Cancer . Score:  0.12818475\n",
      "Class name:  Infrastructure . Score:  0.1275133\n",
      "Class name:  Temporal_Difference . Score:  0.1274427\n",
      "Class name:  Education . Score:  0.12656416\n",
      "Class name:  Pattern_theory . Score:  0.12623371\n",
      "Class name:  Surveillance . Score:  0.12502705\n",
      "Class name:  Statistics . Score:  0.124768406\n",
      "Class name:  Semantics . Score:  0.12465635\n",
      "Class name:  Hyperspectral . Score:  0.123869225\n",
      "Class name:  Bioinformatics . Score:  0.12381881\n",
      "Class name:  Business . Score:  0.12315393\n",
      "Class name:  Entertainment . Score:  0.12116782\n",
      "Class name:  Housing . Score:  0.119603835\n",
      "Class name:  GoogleNet/Inception . Score:  0.11860311\n",
      "Class name:  Classification . Score:  0.11819188\n",
      "Class name:  Health . Score:  0.117030725\n",
      "Class name:  Demographics . Score:  0.116779596\n",
      "Class name:  Mathematics . Score:  0.11615453\n",
      "Class name:  OPTICS_algorithm . Score:  0.11545669\n",
      "Class name:  Fundamental . Score:  0.11494838\n",
      "Class name:  Genomics . Score:  0.1138469\n",
      "Class name:  Colorization . Score:  0.11287639\n",
      "Class name:  Finance . Score:  0.11089578\n",
      "Class name:  Astronomy . Score:  0.109343864\n",
      "Class name:  Ecology . Score:  0.10788293\n",
      "Class name:  Economics . Score:  0.10759974\n",
      "Class name:  Robotics . Score:  0.10744331\n",
      "Class name:  neuroscience . Score:  0.10688015\n",
      "Class name:  Divisive . Score:  0.10549636\n",
      "Class name:  ZFNet . Score:  0.1052417\n",
      "Class name:  Microbiology . Score:  0.10458554\n",
      "Class name:  Agriculture . Score:  0.10385771\n",
      "Class name:  NeuroSolutions . Score:  0.10381601\n",
      "Class name:  Geospatial . Score:  0.1021367\n",
      "Class name:  Tax . Score:  0.10152704\n",
      "Class name:  Geography . Score:  0.10147193\n",
      "Class name:  Security . Score:  0.101065435\n",
      "Class name:  Film . Score:  0.09922793\n",
      "Class name:  Epidemiology . Score:  0.09841904\n",
      "Class name:  Resilient_backpropagation_(Rprop) . Score:  0.097042456\n",
      "Class name:  Weather . Score:  0.096727036\n",
      "Class name:  Media . Score:  0.09607028\n",
      "Class name:  emissions . Score:  0.09575661\n",
      "Class name:  Multidimensional_Scaling_(MDS) . Score:  0.09466919\n",
      "Class name:  pollution . Score:  0.09419012\n",
      "Class name:  Law . Score:  0.090173334\n",
      "Class name:  LPBoost . Score:  0.08728944\n",
      "Class name:  Genetics . Score:  0.086575665\n",
      "Class name:  Lemmatization . Score:  0.08571947\n",
      "Class name:  Journalism . Score:  0.08351354\n",
      "Class name:  A*_Search . Score:  0.06979811\n",
      "Class name:  Backpropogation . Score:  0.06572671\n",
      "Class name:  Chatbots . Score:  0.06496565\n",
      "Class name:  SQLServerAnalysisServices . Score:  0.061175164\n",
      "Class name:  Quantization . Score:  0.057140224\n",
      "Class name:  Climate . Score:  0.054062005\n",
      "Class name:  Meteorology . Score:  0.05313485\n",
      "Class name:  ApacheMahout . Score:  0.050007645\n",
      "Class name:  Perceptrons . Score:  0.030244712\n"
     ]
    }
   ],
   "source": [
    "results_fastText = fastTextSearch(query, fasttextModel)\n",
    "for result in results_fastText:\n",
    "    print('Class name: ', result[0].name, '. Score: ', result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjList = dict()\n",
    "for c in class_list:\n",
    "    adjList[c.name] = [s.name for s in c.subclasses()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.from_dict_of_lists(adjList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes:  1084 . Number of edges:  1100\n"
     ]
    }
   ],
   "source": [
    "print('Number of nodes: ', len(list(G.nodes)), '. Number of edges: ', len(list(G.edges())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing transition probabilities: 100%|██████████| 1084/1084 [00:00<00:00, 4911.62it/s]\n",
      "Generating walks (CPU: 1): 100%|██████████| 10/10 [00:42<00:00,  4.54s/it]\n"
     ]
    }
   ],
   "source": [
    "from node2vec import Node2Vec\n",
    "node2vec = Node2Vec(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = node2vec.fit()              # returns a gensim wv model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('DBM', 0.9310755729675293),\n",
       " ('RBM', 0.8851877450942993),\n",
       " ('Binary_RBM_with_Contrastive_Divergence', 0.84473717212677),\n",
       " ('Hebbian_Learning', 0.8392605781555176),\n",
       " ('Perceptrons', 0.835342526435852),\n",
       " ('Artificial_Neural_Networks', 0.8352882862091064),\n",
       " ('Binary_RBM_with_Persistent_Contrastive_Divergence', 0.8196649551391602),\n",
       " ('Hopfield_Network', 0.8088248372077942),\n",
       " ('Multilayer_Feed-Forward_Networks', 0.7006083130836487),\n",
       " ('Neural_Networks', 0.6821851134300232)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar('Boltzmann_Machine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set([c[0] for c in onto.get_triples()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj  Absolute_Error_Loss prop  []\n",
      "Obj  Activation_Functions prop  []\n",
      "Obj  AdaDelta prop  []\n",
      "Obj  Adagrad prop  []\n",
      "Obj  Adam prop  []\n",
      "Obj  Agriculture prop  []\n",
      "Obj  Ant_colony_optimization prop  []\n",
      "Obj  ArSinH_Function prop  []\n",
      "Obj  ArcTan_Function prop  []\n",
      "Obj  Architecture prop  []\n",
      "Obj  Artificial_Bee_Colony_Optimization prop  []\n",
      "Obj  Artificial_Fish_Swarm_Algorithm prop  []\n",
      "Obj  Artificial_Intelligence prop  []\n",
      "Obj  Artificial_Neural_Networks prop  []\n",
      "Obj  Arts_and_Entertainment prop  []\n",
      "Obj  Assistance prop  []\n",
      "Obj  Astronomy prop  []\n",
      "Obj  Automobiles prop  []\n",
      "Obj  Autonomous_Vehicles prop  []\n",
      "Obj  Backpropogation prop  []\n",
      "Obj  Bacterial_Foraging_Optimization prop  []\n",
      "Obj  Banking prop  []\n",
      "Obj  Behavior_based_AI prop  []\n",
      "Obj  Bent_identity_Function prop  []\n",
      "Obj  Best_First_Search prop  []\n",
      "Obj  Bidirectional_Search prop  []\n",
      "Obj  Big_Data prop  []\n",
      "Obj  Binary_Classification_Loss_Functions prop  []\n",
      "Obj  Binary_sigmoidal_function prop  []\n",
      "Obj  Binary_step_function prop  []\n",
      "Obj  Bioinformatics prop  []\n",
      "Obj  Biology prop  []\n",
      "Obj  Bipolar_sigmoidal_function prop  []\n",
      "Obj  Bipolar_step_function prop  []\n",
      "Obj  Blind_Search prop  []\n",
      "Obj  Bots prop  []\n",
      "Obj  Breadth-first_search prop  []\n",
      "Obj  Brute_Force_Search prop  []\n",
      "Obj  Business prop  []\n",
      "Obj  Cancer prop  []\n",
      "Obj  Cell_Imaging prop  []\n",
      "Obj  Cell_Painting prop  []\n",
      "Obj  Cell_biology prop  []\n",
      "Obj  Census prop  []\n",
      "Obj  Chatbots prop  []\n",
      "Obj  Classification_Loss_Functions prop  []\n",
      "Obj  Climate prop  []\n",
      "Obj  Computational_creativity prop  []\n",
      "Obj  Computer_Security prop  []\n",
      "Obj  Construction prop  []\n",
      "Obj  Conversational_Bot prop  []\n",
      "Obj  Cosine_Error prop  []\n",
      "Obj  Crime prop  []\n",
      "Obj  Cross_Entropy_Loss prop  []\n",
      "Obj  Culture_and_Humanities prop  []\n",
      "Obj  Cyber_Security prop  []\n",
      "Obj  DNA_sequencing prop  []\n",
      "Obj  Data_Analytics prop  []\n",
      "Obj  Data_Science prop  []\n",
      "Obj  Data_mining prop  []\n",
      "Obj  Demographics prop  []\n",
      "Obj  Dempster-Shafer_Theory prop  []\n",
      "Obj  Depth-first_search prop  []\n",
      "Obj  Design_and_Modelling prop  []\n",
      "Obj  Diabetes prop  []\n",
      "Obj  Digital_Preservation prop  []\n",
      "Obj  Digital_media prop  []\n",
      "Obj  Disaster_Management prop  []\n",
      "Obj  Distributed_Artificial_Intelligence prop  []\n",
      "Obj  Ecology prop  []\n",
      "Obj  Economics prop  []\n",
      "Obj  Education prop  []\n",
      "Obj  ElliotSig_Function prop  []\n",
      "Obj  Embedding_loss_functions prop  []\n",
      "Obj  Entertainment prop  []\n",
      "Obj  Entropy_based_Thresholding prop  []\n",
      "Obj  Epidemiology prop  []\n",
      "Obj  Evolutionary_Computation prop  []\n",
      "Obj  Feature_Engineering prop  []\n",
      "Obj  Film prop  []\n",
      "Obj  Finance prop  []\n",
      "Obj  First_Order_Optimization_Algorithms prop  []\n",
      "Obj  Flower_Pollination_Optimization prop  []\n",
      "Obj  Fluorescence_imaging prop  []\n",
      "Obj  Fuzzy_Logic prop  []\n",
      "Obj  Fuzzy_Sets prop  []\n",
      "Obj  Fuzzy_Systems prop  []\n",
      "Obj  GRU prop  []\n",
      "Obj  Gaussian_Function prop  []\n",
      "Obj  Genetic_Algorithm_Optimization prop  []\n",
      "Obj  Genetics prop  []\n",
      "Obj  Genomics prop  []\n",
      "Obj  Geography prop  []\n",
      "Obj  Geospatial prop  []\n",
      "Obj  Global_Valley_approach prop  []\n",
      "Obj  Gradient_Descent prop  []\n",
      "Obj  Gravitational_Search_Algorithm prop  []\n",
      "Obj  Greedy_Search prop  []\n",
      "Obj  Health prop  []\n",
      "Obj  Heuristic_Search prop  []\n",
      "Obj  High-throughput_imaging prop  []\n",
      "Obj  Hill-Climbing_Search prop  []\n",
      "Obj  Hinge_Loss prop  []\n",
      "Obj  Housing prop  []\n",
      "Obj  Huber_Loss prop  []\n",
      "Obj  Human_Computer_Interaction prop  []\n",
      "Obj  Identity_function prop  []\n",
      "Obj  Image_Processing prop  []\n",
      "Obj  Income prop  []\n",
      "Obj  Infrastructure prop  []\n",
      "Obj  Intelligent_Water_Drops_Algorithm prop  []\n",
      "Obj  Intrusion_Detection prop  []\n",
      "Obj  Iterative_deepening_depth-first_search prop  []\n",
      "Obj  Journalism prop  []\n",
      "Obj  Kullback_Leibler_Divergence_Loss prop  []\n",
      "Obj  L1_Hinge_Error prop  []\n",
      "Obj  L1_Loss prop  []\n",
      "Obj  L2_Loss prop  []\n",
      "Obj  Law prop  []\n",
      "Obj  Levenberg-Marquardt_backpropagation prop  []\n",
      "Obj  Life_sciences prop  []\n",
      "Obj  Literature prop  []\n",
      "Obj  Local_Beam_Search prop  []\n",
      "Obj  Local_Search prop  []\n",
      "Obj  Logic_Based_Reasoning prop  []\n",
      "Obj  Logistic_Function prop  []\n",
      "Obj  Manufacturing prop  []\n",
      "Obj  Margin_Classifier prop  []\n",
      "Obj  Marketing prop  []\n",
      "Obj  Mathematics prop  []\n",
      "Obj  Maxout_Function prop  []\n",
      "Obj  Mean_Bias_Error prop  []\n",
      "Obj  Media prop  []\n",
      "Obj  Medicine prop  []\n",
      "Obj  Meteorology prop  []\n",
      "Obj  Microbiology prop  []\n",
      "Obj  Migrating_Birds_Optimization prop  []\n",
      "Obj  Mini-Max_Algorithm prop  []\n",
      "Obj  Mini_Batch_Gradient_Descent prop  []\n",
      "Obj  Miscellaneous_AI_applications prop  []\n",
      "Obj  Molecular_Docking prop  []\n",
      "Obj  Molecular_biology prop  []\n",
      "Obj  Movies prop  []\n",
      "Obj  Multi-class_Classification_Loss_Functions prop  []\n",
      "Obj  Multi-class_Cross_Entropy_Loss prop  []\n",
      "Obj  Multiclass_Classification prop  []\n",
      "Obj  Music prop  []\n",
      "Obj  Nature_and_life_sciences prop  []\n",
      "Obj  Negative_Log_Likelihood prop  []\n",
      "Obj  Nesterov_accelerated_gradient prop  []\n",
      "Obj  News prop  []\n",
      "Obj  Nouvelle_AI prop  []\n",
      "Obj  Optical_imagery prop  []\n",
      "Obj  Optimization_Algorithms prop  []\n",
      "Obj  Particle_swarm_optimization prop  []\n",
      "Obj  Performance_Metrics prop  []\n",
      "Obj  Personal_Assistant prop  []\n",
      "Obj  Pharmaceutical prop  []\n",
      "Obj  Photography prop  []\n",
      "Obj  Politics prop  []\n",
      "Obj  Probability prop  []\n",
      "Obj  Pure_Heuristic_Search prop  []\n",
      "Obj  Quadratic_Loss prop  []\n",
      "Obj  Ramp_function prop  []\n",
      "Obj  Recommendation_Systems prop  []\n",
      "Obj  Regression_Loss_Functions prop  []\n",
      "Obj  Remote_Sensing prop  []\n",
      "Obj  Revenue_Forecast prop  []\n",
      "Obj  SQ-RBF_Function prop  []\n",
      "Obj  SQNL_Function prop  []\n",
      "Obj  Search_Algorithms prop  []\n",
      "Obj  Second_Order_Optimization_Algorithms prop  []\n",
      "Obj  Security prop  []\n",
      "Obj  Sigmoid_Function prop  []\n",
      "Obj  Sigmoidal_function prop  []\n",
      "Obj  Simulated_Annealing prop  []\n",
      "Obj  Sinc_Function prop  []\n",
      "Obj  Sinusoid_Function prop  []\n",
      "Obj  Sociology prop  []\n",
      "Obj  SoftExponential_Function prop  []\n",
      "Obj  SoftPlus_Function prop  []\n",
      "Obj  Soft_Clipping_Function prop  []\n",
      "Obj  Soft_Margin_Classifier prop  []\n",
      "Obj  Softmax_Function prop  []\n",
      "Obj  Softsign_Function prop  []\n",
      "Obj  Softstep_Function prop  []\n",
      "Obj  Speech_denoising prop  []\n",
      "Obj  Speech_processing prop  []\n",
      "Obj  Sports prop  []\n",
      "Obj  Squared_Error_Loss prop  []\n",
      "Obj  Statistical_AI prop  []\n",
      "Obj  Statistics prop  []\n",
      "Obj  Stochastic_gradient_descent prop  []\n",
      "Obj  Subsumption_Architecture prop  []\n",
      "Obj  Surveillance prop  []\n",
      "Obj  Survey prop  []\n",
      "Obj  Tabu_Search_Algorithm prop  []\n",
      "Obj  TanH__Function prop  []\n",
      "Obj  Tax prop  []\n",
      "Obj  Terrain_Mapping prop  []\n",
      "Obj  Text_Mining prop  []\n",
      "Obj  Trade prop  []\n",
      "Obj  Traffic prop  []\n",
      "Obj  Transfer_Learning prop  []\n",
      "Obj  Transport prop  []\n",
      "Obj  Transportation prop  []\n",
      "Obj  Travelling_Salesman_Problem prop  []\n",
      "Obj  Uniform_cost_search prop  []\n",
      "Obj  Urban_Development prop  []\n",
      "Obj  Variance_based_Thresholding prop  []\n",
      "Obj  Verbal_Assistant prop  []\n",
      "Obj  Video_games prop  []\n",
      "Obj  Visual_arts prop  []\n",
      "Obj  Weather prop  []\n",
      "Obj  aerial_imagery prop  []\n",
      "Obj  culture prop  []\n",
      "Obj  disaster_response prop  []\n",
      "Obj  emissions prop  []\n",
      "Obj  environmental prop  []\n",
      "Obj  forecast_system prop  []\n",
      "Obj  genes_prediction prop  []\n",
      "Obj  imaging prop  []\n",
      "Obj  microscopy prop  []\n",
      "Obj  multimedia prop  []\n",
      "Obj  neuro_biology prop  []\n",
      "Obj  neuro_imaging prop  []\n",
      "Obj  neuroscience prop  []\n",
      "Obj  pollution prop  []\n",
      "Obj  quality prop  []\n",
      "Obj  radiations prop  []\n",
      "Obj  warning_systems prop  []\n",
      "Obj  A*_Search prop  []\n",
      "Obj  Adaptive_piecewise_linear_(APL) prop  []\n",
      "Obj  Binary_Cross-Entropy_(BCE) prop  []\n",
      "Obj  Bipolar_rectified_linear_unit_(BReLU) prop  []\n",
      "Obj  Exponential_linear_unit_(ELU) prop  []\n",
      "Obj  Gaussian_Error_Linear_Units_(GELU) prop  []\n",
      "Obj  Geographic_information_system_(GIS) prop  []\n",
      "Obj  Geoinformatics_(GIS) prop  []\n",
      "Obj  Inverse_square_root_linear_unit_(ISRLU) prop  []\n",
      "Obj  Inverse_square_root_unit_(ISRU) prop  []\n",
      "Obj  Iterative_deepening_A*_Search prop  []\n",
      "Obj  Leaky_rectified_linear_unit_(Leaky_ReLU) prop  []\n",
      "Obj  Mean_Absolute_Error_(MAE) prop  []\n",
      "Obj  Mean_Square_Error_(MSE) prop  []\n",
      "Obj  Parameteric_rectified_linear_unit_(PReLU) prop  []\n",
      "Obj  Randomized_leaky_rectified_linear_unit_(RReLU) prop  []\n",
      "Obj  Rectified_linear_unit_(ReLU) prop  []\n",
      "Obj  Resilient_backpropagation_(Rprop) prop  []\n",
      "Obj  S-shaped_rectified_linear_activation_unit_(SReLU) prop  []\n",
      "Obj  Satellite_Imaging/Satellite_Imagery prop  []\n",
      "Obj  Scaled_exponential_linear_unit_(SELU) prop  []\n",
      "Obj  Sigmoid_Linear_Unit_(SiLU) prop  []\n",
      "Obj  AHE prop  []\n",
      "Obj  AIML prop  []\n",
      "Obj  AI_Approaches prop  []\n",
      "Obj  AI_Concepts prop  []\n",
      "Obj  AI_Methodoogy prop  []\n",
      "Obj  AI_Ontology prop  []\n",
      "Obj  AI_Programming_Language prop  []\n",
      "Obj  AI_Software_Library prop  []\n",
      "Obj  ATC prop  []\n",
      "Obj  AZURE prop  []\n",
      "Obj  Accented_Speech_Recognition prop  []\n",
      "Obj  Accord.net prop  []\n",
      "Obj  Accuracy_Metrics prop  []\n",
      "Obj  Acoustic_Modelling prop  []\n",
      "Obj  Acoustic_Question_Answering prop  []\n",
      "Obj  Action_Classification prop  []\n",
      "Obj  Action_Detection prop  []\n",
      "Obj  Action_Localization prop  []\n",
      "Obj  Active_Learning prop  []\n",
      "Obj  Active_Object_Localization prop  []\n",
      "Obj  Activity_Recognition prop  []\n",
      "Obj  Ad_Targeting prop  []\n",
      "Obj  AdaBoost prop  []\n",
      "Obj  Adaptive_Thresholding prop  []\n",
      "Obj  Adversarial_Autoencoder prop  []\n",
      "Obj  Adversarial_Variational_Bayes prop  []\n",
      "Obj  Affective_Computing prop  []\n",
      "Obj  Affinity_Propogation prop  []\n",
      "Obj  Affix_stemmers prop  []\n",
      "Obj  Agents_and_Environment prop  []\n",
      "Obj  Agglomerative prop  []\n",
      "Obj  AlexNet prop  []\n",
      "Obj  Algorithmic_trading prop  []\n",
      "Obj  Analogy prop  []\n",
      "Obj  Anamoly_Detection prop  []\n",
      "Obj  Animation prop  []\n",
      "Obj  Anomaly_Detection prop  []\n",
      "Obj  Anomaly_Detection_In_Surveillance_Videos prop  []\n",
      "Obj  ApacheMahout prop  []\n",
      "Obj  Applications prop  []\n",
      "Obj  Art_Analysis prop  []\n",
      "Obj  Articulatory_synthesis prop  []\n",
      "Obj  Arts prop  []\n",
      "Obj  Audio-Visual_Speech_Recognition prop  []\n",
      "Obj  Audio-Visual_Synchronization prop  []\n",
      "Obj  Audio-Visual_Video_Captioning prop  []\n",
      "Obj  Audit prop  []\n",
      "Obj  AutoML prop  []\n",
      "Obj  Autoencoder prop  []\n",
      "Obj  Autoencoders prop  []\n",
      "Obj  Automated_Feature_Engineering prop  []\n",
      "Obj  Automatic_Relevance_Determination prop  []\n",
      "Obj  Automatic_Summarization prop  []\n",
      "Obj  Automotive prop  []\n",
      "Obj  Autonomous_Vehicles prop  []\n",
      "Obj  Auxiliary_Classifier_GAN prop  []\n",
      "Obj  Auxiliary_Learning prop  []\n",
      "Obj  Backward_Feature_Elimination prop  []\n",
      "Obj  Basic_Image_Filtering prop  []\n",
      "Obj  Bayesian_Inference prop  []\n",
      "Obj  Bayesian_Network prop  []\n",
      "Obj  Bayesian_Regression prop  []\n",
      "Obj  Bayesian_Ridge_Regression prop  []\n",
      "Obj  Belief-network prop  []\n",
      "Obj  Bidirectional_RNN prop  []\n",
      "Obj  Binary_RBM_with_Contrastive_Divergence prop  []\n",
      "Obj  Binary_RBM_with_Persistent_Contrastive_Divergence prop  []\n",
      "Obj  Binary_Shape_Analysis prop  []\n",
      "Obj  Birch prop  []\n",
      "Obj  Bird_View_Synthesis prop  []\n",
      "Obj  Blind_Image_Deblurring prop  []\n",
      "Obj  Blind_Image_Quality_Assessment prop  []\n",
      "Obj  Boltzmann_Machine prop  []\n",
      "Obj  Boosting prop  []\n",
      "Obj  Boundary_Detection prop  []\n",
      "Obj  Boundary_Equilibrium_GAN prop  []\n",
      "Obj  Boundary_Pattern_Analysis prop  []\n",
      "Obj  Boundary_Seeking_GAN prop  []\n",
      "Obj  Boundary_Tracking prop  []\n",
      "Obj  Brill_tagger prop  []\n",
      "Obj  BrownBoost prop  []\n",
      "Obj  Byte_pair_encoding prop  []\n",
      "Obj  CLAHE prop  []\n",
      "Obj  CNN prop  []\n",
      "Obj  CPlusPlus prop  []\n",
      "Obj  C_Language prop  []\n",
      "Obj  Caffe prop  []\n",
      "Obj  Camera_Localization prop  []\n",
      "Obj  Camera_Relocalization prop  []\n",
      "Obj  Car_Racing prop  []\n",
      "Obj  CatBOOST prop  []\n",
      "Obj  CatBoost prop  []\n",
      "Obj  Centura prop  []\n",
      "Obj  Circle_and_Ellipse_Detection prop  []\n",
      "Obj  Classification prop  []\n",
      "Obj  Classification_Methods prop  []\n",
      "Obj  Clinical_training prop  []\n",
      "Obj  Clustering prop  []\n",
      "Obj  Clustering_Methods prop  []\n",
      "Obj  Co-Saliency_Detection prop  []\n",
      "Obj  Cognitive_simulation prop  []\n",
      "Obj  Collaborative_Filtering prop  []\n",
      "Obj  Color_Constancy prop  []\n",
      "Obj  Color_Image_Compression_Artifact_Reduction prop  []\n",
      "Obj  Colorization prop  []\n",
      "Obj  Combinatorial_Optimization prop  []\n",
      "Obj  Computational_intelligence prop  []\n",
      "Obj  Computer-aided_interpretation_of_medical_images prop  []\n",
      "Obj  Computer_Vision prop  []\n",
      "Obj  Concatenation_synthesis prop  []\n",
      "Obj  Conditional_Image_Generation prop  []\n",
      "Obj  Conditional_Planning prop  []\n",
      "Obj  Conditional_VAE prop  []\n",
      "Obj  Conditional_random_fields_approach prop  []\n",
      "Obj  Constrained_Diffeomorphic_Image_Registration prop  []\n",
      "Obj  Content-Based_Filtering prop  []\n",
      "Obj  Content-Based_Image_Retrieval prop  []\n",
      "Obj  Content_Generation prop  []\n",
      "Obj  Context_based_Emotion_Recognition prop  []\n",
      "Obj  Continuous_Control prop  []\n",
      "Obj  Continuous_Object_Recognition prop  []\n",
      "Obj  Contour_Detection prop  []\n",
      "Obj  Conversational_Speech_recognition prop  []\n",
      "Obj  Converssational_Emotion_Recognition prop  []\n",
      "Obj  Convolutional_Neural_Networks prop  []\n",
      "Obj  Coreference_Resolution prop  []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj  Corner_and_Interest_Point_Detection prop  []\n",
      "Obj  Coupled_GAN prop  []\n",
      "Obj  Curved_Text_Detection prop  []\n",
      "Obj  Cybernetics_and_brain_simulation prop  []\n",
      "Obj  DBM prop  []\n",
      "Obj  DLLearner prop  []\n",
      "Obj  Data_Augmentation prop  []\n",
      "Obj  Deblurring prop  []\n",
      "Obj  Decision-Tree_Regressor prop  []\n",
      "Obj  Decision_Stump prop  []\n",
      "Obj  Decision_Theory prop  []\n",
      "Obj  Decision_Trees prop  []\n",
      "Obj  Deep_Adversarial_Networks prop  []\n",
      "Obj  Deep_Attention prop  []\n",
      "Obj  Deep_Learning prop  []\n",
      "Obj  Deep_RNN prop  []\n",
      "Obj  Deep_Reinforcement_Learning prop  []\n",
      "Obj  Deep_feedforward_and_recurrent_neural_networks prop  []\n",
      "Obj  Deep_learning_synthesis prop  []\n",
      "Obj  Default_reasoning_and_the_qualification_problem prop  []\n",
      "Obj  Defocus_Estimation prop  []\n",
      "Obj  Deformable_Object_Manipulation prop  []\n",
      "Obj  Demosaicking prop  []\n",
      "Obj  Denoising prop  []\n",
      "Obj  Denoising_VAE prop  []\n",
      "Obj  Dense_Pixel_Correspondence_Estimation prop  []\n",
      "Obj  Dense_Video_Captioning prop  []\n",
      "Obj  Density_Based_Spatial_Clustering prop  []\n",
      "Obj  Density_Estimation prop  []\n",
      "Obj  Depiction_Invariant_Object_Recognition prop  []\n",
      "Obj  Depth_And_Camera_Motion prop  []\n",
      "Obj  Depth_Completion prop  []\n",
      "Obj  Depth_Estimation prop  []\n",
      "Obj  Depth_Map_Super-Resolution prop  []\n",
      "Obj  Depth_Prediction_For_Outdoor_Scene prop  []\n",
      "Obj  Description_Logic prop  []\n",
      "Obj  Design_treatment_plans prop  []\n",
      "Obj  Dictionary-based_MT prop  []\n",
      "Obj  Digit_Recognition prop  []\n",
      "Obj  Digital_Image_Processing prop  []\n",
      "Obj  Dimensionality_Reduction prop  []\n",
      "Obj  Dimensionality_Reduction_methods prop  []\n",
      "Obj  Diphone_synthesis prop  []\n",
      "Obj  DiscoGAN prop  []\n",
      "Obj  Discourse prop  []\n",
      "Obj  Disease_prediction_and_diagnosis prop  []\n",
      "Obj  Disparity_Estimation prop  []\n",
      "Obj  Distant_Speech_Recognition prop  []\n",
      "Obj  Distributional_Reinforcement_Learning prop  []\n",
      "Obj  Distributional_Semantics prop  []\n",
      "Obj  Distributional_learning prop  []\n",
      "Obj  Divisive prop  []\n",
      "Obj  Document_Embedding prop  []\n",
      "Obj  Document_Image_Classification prop  []\n",
      "Obj  Document_To_Image_Conversion prop  []\n",
      "Obj  Domain-specific_synthesis prop  []\n",
      "Obj  Domain_Adaptation prop  []\n",
      "Obj  Domain_Generalization prop  []\n",
      "Obj  Drawing_Pictures prop  []\n",
      "Obj  Drug_creation prop  []\n",
      "Obj  DualGAN prop  []\n",
      "Obj  Dynamic_Belief_Networks prop  []\n",
      "Obj  Dynamic_Decision_Networks prop  []\n",
      "Obj  Dynamic_Pricing prop  []\n",
      "Obj  Dynamic_Texture_Recognition prop  []\n",
      "Obj  Dynamic_time_warping prop  []\n",
      "Obj  ELKI prop  []\n",
      "Obj  EViews prop  []\n",
      "Obj  Edge_Detection prop  []\n",
      "Obj  Effector prop  []\n",
      "Obj  Egocentric_Activity_Recognition prop  []\n",
      "Obj  Elastic-Net prop  []\n",
      "Obj  Elastic_net prop  []\n",
      "Obj  Elliptic_Envelope prop  []\n",
      "Obj  Embodied_Question_Answering prop  []\n",
      "Obj  Embodied_intelligence prop  []\n",
      "Obj  Emotion_Recognition prop  []\n",
      "Obj  Encog prop  []\n",
      "Obj  Energy_Based_GAN prop  []\n",
      "Obj  Ensemble_Learning prop  []\n",
      "Obj  Entity_Embeddings prop  []\n",
      "Obj  Event-based_vision prop  []\n",
      "Obj  Evolutionary_algorithms prop  []\n",
      "Obj  Example-based_MT prop  []\n",
      "Obj  Expectation-Maximization prop  []\n",
      "Obj  Expressive_Operator_Descriptions prop  []\n",
      "Obj  FAMa prop  []\n",
      "Obj  Face_Alignment prop  []\n",
      "Obj  Face_Detection prop  []\n",
      "Obj  Face_Generation prop  []\n",
      "Obj  Face_Recognition prop  []\n",
      "Obj  Face_Reconstruction prop  []\n",
      "Obj  Face_Transfer prop  []\n",
      "Obj  Face_Verification prop  []\n",
      "Obj  Facial_Expression_Recognition prop  []\n",
      "Obj  Facial_Inpainting prop  []\n",
      "Obj  Facial_Recognition_and_Modelling prop  []\n",
      "Obj  Factor_Analysis prop  []\n",
      "Obj  Factual_Visual_Question_Answering prop  []\n",
      "Obj  Fake_Image_Detection prop  []\n",
      "Obj  Feature_Importance prop  []\n",
      "Obj  Feature_Learning prop  []\n",
      "Obj  Feature_Selection prop  []\n",
      "Obj  Few-Shot_Camera-Adaptive_Color_Constancy prop  []\n",
      "Obj  Few-Shot_Camera_Adaptive_Color_Constancy prop  []\n",
      "Obj  Few-Shot_Image_Classification prop  []\n",
      "Obj  Few-Shot_Imitation_Learning prop  []\n",
      "Obj  Few-Shot_Learning prop  []\n",
      "Obj  Few-Shot_Relation_Classification prop  []\n",
      "Obj  Finance_and_Economics prop  []\n",
      "Obj  Fine-Grained_Action_Detection prop  []\n",
      "Obj  Fine-Grained_Image_Classification prop  []\n",
      "Obj  Fine-Grained_Image_Recognition prop  []\n",
      "Obj  Fine_Grain_Image_Categorization prop  []\n",
      "Obj  First-order_Logic prop  []\n",
      "Obj  Forgery prop  []\n",
      "Obj  Formant_synthesis prop  []\n",
      "Obj  Forward_Feature_Selection prop  []\n",
      "Obj  Frame_Systems_and_Semantic_Networks prop  []\n",
      "Obj  Fraud_detection prop  []\n",
      "Obj  Fraudalent_ads prop  []\n",
      "Obj  Fundamental prop  []\n",
      "Obj  GAN prop  []\n",
      "Obj  GBM prop  []\n",
      "Obj  GCN prop  []\n",
      "Obj  Game_Theory prop  []\n",
      "Obj  Games prop  []\n",
      "Obj  Gated_Recurrent_Unit prop  []\n",
      "Obj  Gaze_Estimation prop  []\n",
      "Obj  General_Intelligence prop  []\n",
      "Obj  General_Logical_Descriptions prop  []\n",
      "Obj  General_Ontology prop  []\n",
      "Obj  Generalized_Zero-Shot_Learning prop  []\n",
      "Obj  Generative_Adversarial_Networks prop  []\n",
      "Obj  Generative_Adversarial_Parallelization prop  []\n",
      "Obj  Genetic_Algorithms prop  []\n",
      "Obj  Gesture_Recognition prop  []\n",
      "Obj  GibbsNet prop  []\n",
      "Obj  Graddient_Boosting prop  []\n",
      "Obj  Gradient-Boost_Regressor prop  []\n",
      "Obj  Gradient_Boost prop  []\n",
      "Obj  Grammar_Induction prop  []\n",
      "Obj  Grammatical_inference_by_genetic_algorithms prop  []\n",
      "Obj  Grammatical_inference_by_greedy_algorithms prop  []\n",
      "Obj  Grammatical_inference_by_trial-and-error prop  []\n",
      "Obj  Graph_Embedding prop  []\n",
      "Obj  Graph_Representation_Learning prop  []\n",
      "Obj  Group_Activity_Recognition prop  []\n",
      "Obj  Group_Anomaly_Detection prop  []\n",
      "Obj  H2O prop  []\n",
      "Obj  HMM-based_synthesis prop  []\n",
      "Obj  Hand-Gesture_Recognition prop  []\n",
      "Obj  Handwritten_Digit_Recognition prop  []\n",
      "Obj  Haskell prop  []\n",
      "Obj  Health_Care prop  []\n",
      "Obj  Hebbian_Learning prop  []\n",
      "Obj  Hidden_Markov_Models_based prop  []\n",
      "Obj  Hidden_Markov_models prop  []\n",
      "Obj  Hierarchical_Clustering prop  []\n",
      "Obj  Hierarchical_Decomposition prop  []\n",
      "Obj  Hierarchical_Reinforcement_Learning prop  []\n",
      "Obj  High_Correlation_Filter prop  []\n",
      "Obj  Homography_Estimation prop  []\n",
      "Obj  Hopfield_Network prop  []\n",
      "Obj  Hough_Transform prop  []\n",
      "Obj  Huber_Regression prop  []\n",
      "Obj  Human-Object_Interaction_Detection prop  []\n",
      "Obj  Human_Activity_Recognition prop  []\n",
      "Obj  Human_Computer_Interaction prop  []\n",
      "Obj  Hybrid_MT prop  []\n",
      "Obj  Hyperparameter_Optimization prop  []\n",
      "Obj  Hyperspace_Analogue_to_Language prop  []\n",
      "Obj  Hyperspectral prop  []\n",
      "Obj  Hyperspectral_Image_Classification prop  []\n",
      "Obj  Hyperspectral_Image_Segmentation prop  []\n",
      "Obj  Hyperspectral_Unmixing prop  []\n",
      "Obj  Hypothesis_of_creative_patterns prop  []\n",
      "Obj  IBMMiner prop  []\n",
      "Obj  ICA prop  []\n",
      "Obj  Image-Based_Localization prop  []\n",
      "Obj  Image-to-Image_Translation prop  []\n",
      "Obj  Image_Captioning prop  []\n",
      "Obj  Image_Classification prop  []\n",
      "Obj  Image_Clustering prop  []\n",
      "Obj  Image_Comprehension prop  []\n",
      "Obj  Image_Compression prop  []\n",
      "Obj  Image_Cropping prop  []\n",
      "Obj  Image_Declipping prop  []\n",
      "Obj  Image_Deconvolution prop  []\n",
      "Obj  Image_Dehazing prop  []\n",
      "Obj  Image_Denoising prop  []\n",
      "Obj  Image_Enhancement prop  []\n",
      "Obj  Image_Generation prop  []\n",
      "Obj  Image_Imputation prop  []\n",
      "Obj  Image_Inpainting prop  []\n",
      "Obj  Image_Manipulation_Detection prop  []\n",
      "Obj  Image_Matching prop  []\n",
      "Obj  Image_Matting prop  []\n",
      "Obj  Image_Quality_Assessment prop  []\n",
      "Obj  Image_Recognition prop  []\n",
      "Obj  Image_Reconstruction prop  []\n",
      "Obj  Image_Rectification prop  []\n",
      "Obj  Image_Registration prop  []\n",
      "Obj  Image_Restoration prop  []\n",
      "Obj  Image_Retrieval prop  []\n",
      "Obj  Image_Similarity_Search prop  []\n",
      "Obj  Image_Steganography prop  []\n",
      "Obj  Image_Stitching prop  []\n",
      "Obj  Image_Stylization prop  []\n",
      "Obj  Image_Super-Resolution prop  []\n",
      "Obj  Image_Transformations_and_Camera_Calibration prop  []\n",
      "Obj  Image_categorization prop  []\n",
      "Obj  Imitation_Learning prop  []\n",
      "Obj  Improved_Training_for_Wasserstein_GAN prop  []\n",
      "Obj  Inductive_Learning prop  []\n",
      "Obj  InfoGAN prop  []\n",
      "Obj  Information_Theory prop  []\n",
      "Obj  Infrared_And_Visible_Image_Fusion prop  []\n",
      "Obj  Instance_Search prop  []\n",
      "Obj  Instance_Segmentation prop  []\n",
      "Obj  Intelligent_Agent prop  []\n",
      "Obj  Intelligent_Surveillance prop  []\n",
      "Obj  Intent_Segmentation prop  []\n",
      "Obj  Interactive_Segmentation prop  []\n",
      "Obj  Interlingual_MT prop  []\n",
      "Obj  Intermediate-Level_Vision prop  []\n",
      "Obj  Interpretable_Machine_Learning prop  []\n",
      "Obj  Intrinsic_Image_Decomposition prop  []\n",
      "Obj  Invariants_and_Perspective prop  []\n",
      "Obj  Isolated_Gestures_Recognition prop  []\n",
      "Obj  Java prop  []\n",
      "Obj  Joke_generation prop  []\n",
      "Obj  Jpeg_Compression_Artifact_Reduction prop  []\n",
      "Obj  Julia prop  []\n",
      "Obj  K-Means_Clustering prop  []\n",
      "Obj  KNIME prop  []\n",
      "Obj  KXEN prop  []\n",
      "Obj  Keras prop  []\n",
      "Obj  Kernel_Methods prop  []\n",
      "Obj  Kernel_SVM prop  []\n",
      "Obj  Keypoint_Localization prop  []\n",
      "Obj  Keyword_Spotting prop  []\n",
      "Obj  Knowledge_Engineering prop  []\n",
      "Obj  Knowledge_Graph_Embedding prop  []\n",
      "Obj  Knowledge_and_Reasoning prop  []\n",
      "Obj  Knowledge_based prop  []\n",
      "Obj  Known-Environment prop  []\n",
      "Obj  Known_Environment prop  []\n",
      "Obj  LARS_Lasso prop  []\n",
      "Obj  LIONsolver prop  []\n",
      "Obj  LPBoost prop  []\n",
      "Obj  LSTM prop  []\n",
      "Obj  Language-Based_Temporal_Localization prop  []\n",
      "Obj  Large-Scale_Person_Re-Identification prop  []\n",
      "Obj  Large_Vocabulary_Continuous_Speech_Recognition prop  []\n",
      "Obj  Lasso_Regression prop  []\n",
      "Obj  Latent_semantic_analysis prop  []\n",
      "Obj  LeNet prop  []\n",
      "Obj  Learning_from_Observations prop  []\n",
      "Obj  Least_Angle_Regression prop  []\n",
      "Obj  Least_Squares_GAN prop  []\n",
      "Obj  Lemmatisation_algorithms prop  []\n",
      "Obj  Lemmatization prop  []\n",
      "Obj  Lempel-Ziv-Welch_algorithm prop  []\n",
      "Obj  Lexeme-based_morphology prop  []\n",
      "Obj  Lexical_Semantics prop  []\n",
      "Obj  Lexical_recognition prop  []\n",
      "Obj  LibLinear prop  []\n",
      "Obj  LibSVM prop  []\n",
      "Obj  LightGBM prop  []\n",
      "Obj  Light_GBM prop  []\n",
      "Obj  Line_Detection prop  []\n",
      "Obj  Line_segment_detection prop  []\n",
      "Obj  Linear_Regression prop  []\n",
      "Obj  Linguistics prop  []\n",
      "Obj  Lipreading prop  []\n",
      "Obj  Lisp prop  []\n",
      "Obj  Local_Color_Enhancement prop  []\n",
      "Obj  Local_Invariant_Feature_Detectors_and_Descriptors prop  []\n",
      "Obj  Local_Outlier_Factor prop  []\n",
      "Obj  Logic_based prop  []\n",
      "Obj  Logical_reasoning prop  []\n",
      "Obj  Logistic_Regression prop  []\n",
      "Obj  LogitBoost prop  []\n",
      "Obj  Loss_Function prop  []\n",
      "Obj  Lossy-Compression_Artifact_Reduction prop  []\n",
      "Obj  Low-Level_Vision prop  []\n",
      "Obj  Low-Light_Image_Enhancement prop  []\n",
      "Obj  Low_Variance_Filter prop  []\n",
      "Obj  MAGAN prop  []\n",
      "Obj  MATLAB prop  []\n",
      "Obj  MLPACK prop  []\n",
      "Obj  MOA prop  []\n",
      "Obj  MX-Net prop  []\n",
      "Obj  Machine_Learning prop  []\n",
      "Obj  Machine_Translation prop  []\n",
      "Obj  Machine_Vision prop  []\n",
      "Obj  Manner_Of_Articulation_Detection prop  []\n",
      "Obj  Markerless_Motion_Capture prop  []\n",
      "Obj  Market_analysis_and_data_mining prop  []\n",
      "Obj  Marketing_Forecasting prop  []\n",
      "Obj  Marketing_and_Advertising prop  []\n",
      "Obj  Mask-RCNN prop  []\n",
      "Obj  Matching_algorithms prop  []\n",
      "Obj  Material_Classification prop  []\n",
      "Obj  Material_Recognition prop  []\n",
      "Obj  Materials_Screening prop  []\n",
      "Obj  Mathematica prop  []\n",
      "Obj  Mathematical_Morphology prop  []\n",
      "Obj  Mathematical_question_answering prop  []\n",
      "Obj  Mean-Shift_Clustering prop  []\n",
      "Obj  Medication_management prop  []\n",
      "Obj  Meta-Learning prop  []\n",
      "Obj  Metamerism prop  []\n",
      "Obj  Metaphor_and_simile prop  []\n",
      "Obj  Methods_Based_on_Projections prop  []\n",
      "Obj  Metric_Learning prop  []\n",
      "Obj  Microsoft_CNTK prop  []\n",
      "Obj  Military prop  []\n",
      "Obj  Missing_Markers_Reconstruction prop  []\n",
      "Obj  Missing_Value_Ratio prop  []\n",
      "Obj  Mixture_Model prop  []\n",
      "Obj  Modal_Logic prop  []\n",
      "Obj  Mode_Regularized_GAN prop  []\n",
      "Obj  Model_Compression prop  []\n",
      "Obj  Model_Selection prop  []\n",
      "Obj  Monocular_Depth_Estimation prop  []\n",
      "Obj  MonteCarloMachineLearningLibrary prop  []\n",
      "Obj  Morpheme-based_morphology prop  []\n",
      "Obj  Morphological_Segmentation prop  []\n",
      "Obj  Motion prop  []\n",
      "Obj  Motion_Capture prop  []\n",
      "Obj  Motion_Estimation prop  []\n",
      "Obj  Motion_Planning prop  []\n",
      "Obj  Motion_Segmentation prop  []\n",
      "Obj  Multi-Goal_Reinforcement_Learning prop  []\n",
      "Obj  Multi-Label_Image_Retrieval prop  []\n",
      "Obj  Multi-Layer_Perceptron prop  []\n",
      "Obj  Multi-Oriented_Scene_Text_Detection prop  []\n",
      "Obj  Multi-Task_Learning prop  []\n",
      "Obj  Multi-agent_Reinforcement_Learning prop  []\n",
      "Obj  Multi-modal_image_segmentation prop  []\n",
      "Obj  Multilayer_Feed-Forward_Networks prop  []\n",
      "Obj  Multimodal_Emotion_Recognition prop  []\n",
      "Obj  Multimodal_Unsupervised_Image-To-Image_Translation prop  []\n",
      "Obj  Multiple_Action_Detection prop  []\n",
      "Obj  Multiple_Instance_Learning prop  []\n",
      "Obj  Multiple_Object_Tracking prop  []\n",
      "Obj  Multiview_Learning prop  []\n",
      "Obj  Music_Genre_Transfer prop  []\n",
      "Obj  Musical prop  []\n",
      "Obj  Naive_Bayes_Classifier prop  []\n",
      "Obj  Named-Entity_Recognition prop  []\n",
      "Obj  Natural_Language_Generation prop  []\n",
      "Obj  Natural_Language_Processing prop  []\n",
      "Obj  Natural_Language_Understanding prop  []\n",
      "Obj  Nearest_Neighbor prop  []\n",
      "Obj  Neologism prop  []\n",
      "Obj  Network_Embedding prop  []\n",
      "Obj  Network_Pruning prop  []\n",
      "Obj  Neural-network_based prop  []\n",
      "Obj  Neural_Architecture_Search prop  []\n",
      "Obj  Neural_MT prop  []\n",
      "Obj  Neural_Networks prop  []\n",
      "Obj  NeuroSolutions prop  []\n",
      "Obj  No-Reference_Image_Quality_Assessment prop  []\n",
      "Obj  Noisy_Speech_Recognition prop  []\n",
      "Obj  Non-Monotonic_Logic prop  []\n",
      "Obj  Non-Optimization_based prop  []\n",
      "Obj  Novel_View_Synthesis prop  []\n",
      "Obj  OCR prop  []\n",
      "Obj  OPTICS_algorithm prop  []\n",
      "Obj  Object_Classification prop  []\n",
      "Obj  Object_Counting prop  []\n",
      "Obj  Object_Detection prop  []\n",
      "Obj  Object_Labeling_and_Counting prop  []\n",
      "Obj  Object_Localization prop  []\n",
      "Obj  Object_Recognition prop  []\n",
      "Obj  Object_Reconstruction prop  []\n",
      "Obj  Object_Tracking prop  []\n",
      "Obj  Observation_Completion prop  []\n",
      "Obj  Octave prop  []\n",
      "Obj  One-Class_SVM prop  []\n",
      "Obj  One-Shot_Learning prop  []\n",
      "Obj  Ontology prop  []\n",
      "Obj  OpenCV prop  []\n",
      "Obj  OpenNN prop  []\n",
      "Obj  Open_domain_question_answering prop  []\n",
      "Obj  Optical_Character_Recognition prop  []\n",
      "Obj  Optical_Flow prop  []\n",
      "Obj  Optical_Flow_Estimation prop  []\n",
      "Obj  OracleDataMining prop  []\n",
      "Obj  Orange prop  []\n",
      "Obj  Ordinary_Least_Squares_Regression prop  []\n",
      "Obj  Orthogonal_Matching_Pursuit prop  []\n",
      "Obj  Out-of-Distribution_Detection prop  []\n",
      "Obj  Outlier_Detection prop  []\n",
      "Obj  PCA prop  []\n",
      "Obj  Parsing prop  []\n",
      "Obj  Partial-Order_Planning prop  []\n",
      "Obj  Partial_Domain_Adaptation prop  []\n",
      "Obj  Partially_Instantiated_Operators prop  []\n",
      "Obj  Passive_Aggressive_Algorithms prop  []\n",
      "Obj  Passive_Learning prop  []\n",
      "Obj  Pattern_Matching prop  []\n",
      "Obj  Pattern_recognition prop  []\n",
      "Obj  Pattern_theory prop  []\n",
      "Obj  Pedestrian_Detection prop  []\n",
      "Obj  Perception prop  []\n",
      "Obj  Perceptrons prop  []\n",
      "Obj  Perl prop  []\n",
      "Obj  Person_Re-Identification prop  []\n",
      "Obj  Person_Retrieval prop  []\n",
      "Obj  Personal_finance prop  []\n",
      "Obj  Phonotactic_cues prop  []\n",
      "Obj  Photo-To-Caricature_Translation prop  []\n",
      "Obj  Photo_To_Caricature_Translation prop  []\n",
      "Obj  Planning prop  []\n",
      "Obj  PoS_Tagging prop  []\n",
      "Obj  Poetry prop  []\n",
      "Obj  Policy_Gradient_Methods prop  []\n",
      "Obj  Portfolio_management prop  []\n",
      "Obj  Pose_Estimation prop  []\n",
      "Obj  Predictive_Analytics_for_Marketing prop  []\n",
      "Obj  Predictive_Domain_Adaptation prop  []\n",
      "Obj  Predictive_customer_service prop  []\n",
      "Obj  Probabilistic_Programming prop  []\n",
      "Obj  Probabilistic_methods_for_uncertain_reasoning prop  []\n",
      "Obj  Problem_solving prop  []\n",
      "Obj  Products_recognition prop  []\n",
      "Obj  Programmatic_Advertising prop  []\n",
      "Obj  Projection_Pursuit prop  []\n",
      "Obj  Prolog prop  []\n",
      "Obj  Proportional_Logic prop  []\n",
      "Obj  Provide_consultations_and_Companion_robots prop  []\n",
      "Obj  Python prop  []\n",
      "Obj  Pytorch prop  []\n",
      "Obj  Q-Learning prop  []\n",
      "Obj  Quantization prop  []\n",
      "Obj  Question_Answering prop  []\n",
      "Obj  RBM prop  []\n",
      "Obj  RCASE prop  []\n",
      "Obj  RCNN prop  []\n",
      "Obj  RNN prop  []\n",
      "Obj  R_Language prop  []\n",
      "Obj  Radial-basis_network prop  []\n",
      "Obj  Radial_basis_SVM prop  []\n",
      "Obj  Random_Forest prop  []\n",
      "Obj  Random_indexing prop  []\n",
      "Obj  RapidMiner prop  []\n",
      "Obj  Recommendation_Engine prop  []\n",
      "Obj  Recommendation_Systems prop  []\n",
      "Obj  Recurrent_Neural_Networks prop  []\n",
      "Obj  Recursive_Neural_Networks prop  []\n",
      "Obj  Regression prop  []\n",
      "Obj  Reinforcement_Learning prop  []\n",
      "Obj  Relationship_Extraction prop  []\n",
      "Obj  Representation_Learning prop  []\n",
      "Obj  ResNet prop  []\n",
      "Obj  Ridge_Regression prop  []\n",
      "Obj  Robot_Navigation prop  []\n",
      "Obj  Robot_Task_Planning prop  []\n",
      "Obj  Robotic_Grasping prop  []\n",
      "Obj  Robotics prop  []\n",
      "Obj  Robust_Speech_Recognition prop  []\n",
      "Obj  Robustness_regression prop  []\n",
      "Obj  Ruby prop  []\n",
      "Obj  Rule-based_MT prop  []\n",
      "Obj  SAP prop  []\n",
      "Obj  SAS prop  []\n",
      "Obj  SBD prop  []\n",
      "Obj  SPSS prop  []\n",
      "Obj  SQLServerAnalysisServices prop  []\n",
      "Obj  STATISTICA prop  []\n",
      "Obj  SVD prop  []\n",
      "Obj  SVM prop  []\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obj  Saliency_Detection prop  []\n",
      "Obj  Saliency_Prediction prop  []\n",
      "Obj  Salient_Object_Detection prop  []\n",
      "Obj  Salt-And-Pepper_Noise_Removal prop  []\n",
      "Obj  Sammon_Mapping prop  []\n",
      "Obj  Scene-Aware_Dialogue prop  []\n",
      "Obj  Scene_Classification prop  []\n",
      "Obj  Scene_Flow_Estimation prop  []\n",
      "Obj  Scene_Graph_Generation prop  []\n",
      "Obj  Scene_Parsing prop  []\n",
      "Obj  Scene_Recognition prop  []\n",
      "Obj  Scene_Segmentation prop  []\n",
      "Obj  Scene_Text_Detection prop  []\n",
      "Obj  Scene_Text_Recognition prop  []\n",
      "Obj  Scene_Understanding prop  []\n",
      "Obj  Scruffy prop  []\n",
      "Obj  Search_and_optimization prop  []\n",
      "Obj  Self-Organizing_Maps prop  []\n",
      "Obj  Semantic_Segmentation prop  []\n",
      "Obj  Semantic_folding prop  []\n",
      "Obj  Semantics prop  []\n",
      "Obj  Semi-Supervised_Image_Classification prop  []\n",
      "Obj  Semi-Supervised_Learning prop  []\n",
      "Obj  Semi-Supervised_Person_Re-Identification prop  []\n",
      "Obj  Semi-supervised_Video_Object_Segmentation prop  []\n",
      "Obj  Sensor prop  []\n",
      "Obj  Sensor_Modeling prop  []\n",
      "Obj  Sentence_Embeddings prop  []\n",
      "Obj  Sentence_Segmentaion prop  []\n",
      "Obj  Sentiment_Analysis prop  []\n",
      "Obj  Sequence_to_Sequence_Speecg_Recognition prop  []\n",
      "Obj  Sequential_Learning prop  []\n",
      "Obj  Sequitur prop  []\n",
      "Obj  Session-Based_Recommendations prop  []\n",
      "Obj  Shape_recognition prop  []\n",
      "Obj  Shogun prop  []\n",
      "Obj  Sign_Language_Recognition prop  []\n",
      "Obj  Sign_Language_Translation prop  []\n",
      "Obj  Simultaneous_Localization_and_Mapping prop  []\n",
      "Obj  Sinewave_synthesis prop  []\n",
      "Obj  Single-Image_Blind_Deblurring prop  []\n",
      "Obj  Size_Filtering prop  []\n",
      "Obj  Skeleton_Based_Action_Recognition prop  []\n",
      "Obj  Skeletons_and_Thinning prop  []\n",
      "Obj  Sketch-Based_Image_Retrieval prop  []\n",
      "Obj  Sketch_Recognition prop  []\n",
      "Obj  Social_Intelligence prop  []\n",
      "Obj  Soft_Computing prop  []\n",
      "Obj  Softmax_GAN prop  []\n",
      "Obj  Spark-Mlib prop  []\n",
      "Obj  Sparse_Learning prop  []\n",
      "Obj  Spatio-Temporal_Action_Localization prop  []\n",
      "Obj  Speaker_Identification prop  []\n",
      "Obj  Speaker_Recognition prop  []\n",
      "Obj  Speaking_Style_Synthesis prop  []\n",
      "Obj  Spectral_Clustering prop  []\n",
      "Obj  Spectral_Estimation prop  []\n",
      "Obj  Spectrum_Cartography prop  []\n",
      "Obj  Speech prop  []\n",
      "Obj  Speech-Recognition prop  []\n",
      "Obj  Speech-Segmentation prop  []\n",
      "Obj  Speech_Emotion_Recognition prop  []\n",
      "Obj  Speech_Enhancement prop  []\n",
      "Obj  Speech_Recognition prop  []\n",
      "Obj  Speech_Separation prop  []\n",
      "Obj  Speech_Synthesis prop  []\n",
      "Obj  Speech_Verification prop  []\n",
      "Obj  Spoken_Dialogue_Systems prop  []\n",
      "Obj  Spoken_Language_Understanding prop  []\n",
      "Obj  Stacking prop  []\n",
      "Obj  Stata prop  []\n",
      "Obj  Statistical_Methods prop  []\n",
      "Obj  Statistical_based_MT prop  []\n",
      "Obj  Statistical_learning prop  []\n",
      "Obj  Steering_Control prop  []\n",
      "Obj  Stemming prop  []\n",
      "Obj  Stereo_Depth_Estimation prop  []\n",
      "Obj  Stereo_Matching prop  []\n",
      "Obj  Stereo_from_Motion prop  []\n",
      "Obj  Stochastic_Gradient_Descent_Regression prop  []\n",
      "Obj  Stochastic_Optimization prop  []\n",
      "Obj  Stochastic_algorithms prop  []\n",
      "Obj  Story_generation prop  []\n",
      "Obj  Style_Generalization prop  []\n",
      "Obj  Style_Transfer prop  []\n",
      "Obj  Sub-symbolic_based_AI prop  []\n",
      "Obj  Suffix-stripping_algorithms prop  []\n",
      "Obj  Super_Resolution prop  []\n",
      "Obj  Supervised_Learning prop  []\n",
      "Obj  Supervised_Video_Summarization prop  []\n",
      "Obj  Support_Vector_Machine prop  []\n",
      "Obj  Swarm_Intelligence prop  []\n",
      "Obj  Symbolic_based_AI prop  []\n",
      "Obj  Syntax prop  []\n",
      "Obj  Synthetic-to-Real_Translation prop  []\n",
      "Obj  Tasks prop  []\n",
      "Obj  Temporal_Action_Proposal_Generation prop  []\n",
      "Obj  Temporal_Difference prop  []\n",
      "Obj  Temporal_Localization prop  []\n",
      "Obj  Tensorflow prop  []\n",
      "Obj  Terminology_Extraction prop  []\n",
      "Obj  Text-Independent_Speaker_Recognition prop  []\n",
      "Obj  Text-To-Speech_Synthesis prop  []\n",
      "Obj  Text-to-Image_Generation prop  []\n",
      "Obj  Text-to-speech prop  []\n",
      "Obj  Text_Segmentaion prop  []\n",
      "Obj  Text_Spotting prop  []\n",
      "Obj  Textual_Entailment_Recognition prop  []\n",
      "Obj  Texture prop  []\n",
      "Obj  Texture_Classification prop  []\n",
      "Obj  Texture_Synthesis prop  []\n",
      "Obj  The_Kalman_Filter prop  []\n",
      "Obj  Theano prop  []\n",
      "Obj  Theil-Sen_estimator prop  []\n",
      "Obj  Thematics prop  []\n",
      "Obj  Thermal_Infrared_Object_Tracking prop  []\n",
      "Obj  Thresholding prop  []\n",
      "Obj  Time-to-Adjacency_Analysis prop  []\n",
      "Obj  Topic_Segmentation prop  []\n",
      "Obj  Trajectory_Prediction prop  []\n",
      "Obj  Transfer-Learning prop  []\n",
      "Obj  Transfer-based_MT prop  []\n",
      "Obj  Transfer_Learning prop  []\n",
      "Obj  Transfer_Reinforcement_Learning prop  []\n",
      "Obj  Tribox_method prop  []\n",
      "Obj  UMAP prop  []\n",
      "Obj  Unbalanced_Segmentation prop  []\n",
      "Obj  Uncertain_knowledge_and_reasoning prop  []\n",
      "Obj  Underwriting prop  []\n",
      "Obj  Unit_selection_synthesis prop  []\n",
      "Obj  Unknown-Environment prop  []\n",
      "Obj  Unknown_Environment prop  []\n",
      "Obj  Unsupervised_Anomaly_Detection prop  []\n",
      "Obj  Unsupervised_Anomaly_Detection_In_Sound prop  []\n",
      "Obj  Unsupervised_Domain_Adaptation prop  []\n",
      "Obj  Unsupervised_Image-To-Image_Translation prop  []\n",
      "Obj  Unsupervised_Learning prop  []\n",
      "Obj  Unsupervised_Object_Localization prop  []\n",
      "Obj  Unsupervised_Person_Re-Identification prop  []\n",
      "Obj  Unsupervised_Representation_Learning prop  []\n",
      "Obj  Unsupervised_Video_Object_Segmentation prop  []\n",
      "Obj  Unsupervised_Video_Summarization prop  []\n",
      "Obj  Utility_Theory prop  []\n",
      "Obj  VGGNet prop  []\n",
      "Obj  VNLA prop  []\n",
      "Obj  Vanilla_GAN prop  []\n",
      "Obj  Vanilla_VAE prop  []\n",
      "Obj  Variational_Autoencoder prop  []\n",
      "Obj  Video-Based_Person_Re-Identification prop  []\n",
      "Obj  Video_Captioning prop  []\n",
      "Obj  Video_Classification prop  []\n",
      "Obj  Video_Inpainting prop  []\n",
      "Obj  Video_Object_Segmentation prop  []\n",
      "Obj  Video_Prediction prop  []\n",
      "Obj  Video_Quality_Assessment prop  []\n",
      "Obj  Video_Question_Answering prop  []\n",
      "Obj  Video_Saliency_Detection prop  []\n",
      "Obj  Video_Summarization prop  []\n",
      "Obj  Video_Super-Resolution prop  []\n",
      "Obj  Viewpoint_Estimation prop  []\n",
      "Obj  Vision-Language_Navigation prop  []\n",
      "Obj  Visual_Crowd_Analysis prop  []\n",
      "Obj  Visual_Localization prop  []\n",
      "Obj  Visual_Navigation prop  []\n",
      "Obj  Visual_Object_Tracking prop  []\n",
      "Obj  Visual_Odometry prop  []\n",
      "Obj  Visual_Question_Answering prop  []\n",
      "Obj  Visual_Sentiment_Prediction prop  []\n",
      "Obj  Visual_Speech_recognition prop  []\n",
      "Obj  Visual_and_Artistic prop  []\n",
      "Obj  Voice_Conversion prop  []\n",
      "Obj  Voice_Query_Recognition prop  []\n",
      "Obj  WP prop  []\n",
      "Obj  Wasserstein_GAN prop  []\n",
      "Obj  Weakly-Supervised_Object_Localization prop  []\n",
      "Obj  Weakly-Supervised_Semantic_Segmentation prop  []\n",
      "Obj  Weakly_Supervised_Object_Detection prop  []\n",
      "Obj  Weka prop  []\n",
      "Obj  Wide_Baseline_Matching prop  []\n",
      "Obj  Word-Sense_Disambiguation prop  []\n",
      "Obj  Word-based_morphology prop  []\n",
      "Obj  Word_Embeddings prop  []\n",
      "Obj  Word_Segmentation prop  []\n",
      "Obj  XGBOOST prop  []\n",
      "Obj  XGBoost prop  []\n",
      "Obj  YALE prop  []\n",
      "Obj  Yooreeka prop  []\n",
      "Obj  ZFNet prop  []\n",
      "Obj  Zero-Shot_Action_Recognition prop  []\n",
      "Obj  Zero-Shot_Learning prop  []\n",
      "Obj  f-GAN prop  []\n",
      "Obj  image_outpainting prop  []\n",
      "Obj  k-NN prop  []\n",
      "Obj  language-based_assistance prop  []\n",
      "Obj  mlpy prop  []\n",
      "Obj  n-gram_analysis prop  []\n",
      "Obj  problem_solving prop  []\n",
      "Obj  scikit-learn prop  []\n",
      "Obj  structure_regularization_method prop  []\n",
      "Obj  t-SNE prop  []\n",
      "Obj  3D prop  []\n",
      "Obj  3D_Instance_Segmentation prop  []\n",
      "Obj  3D_Object_Classification prop  []\n",
      "Obj  3D_Object_Recognition prop  []\n",
      "Obj  3D_Object_Super-Resolution prop  []\n",
      "Obj  3D_Part_Segmentation prop  []\n",
      "Obj  3D_Pose_Estimation prop  []\n",
      "Obj  3D_Reconstruction prop  []\n",
      "Obj  3D_Semantic_Instance_Segmentation prop  []\n",
      "Obj  3D_Shape_Analysis prop  []\n",
      "Obj  3D_Shape_Representation prop  []\n",
      "Obj  3D_Vision prop  []\n",
      "Obj  3-D_Reconstruction prop  []\n",
      "Obj  Adversarial_Feature_Learning_&_Adversarially_Learned_Inference prop  []\n",
      "Obj  Bootstrap_Aggregating_(Bagging) prop  []\n",
      "Obj  Conditional_GAN_(CGAN) prop  []\n",
      "Obj  Deep_Belief_Networks_(DBNs) prop  []\n",
      "Obj  Deep_Convolutional_GAN_(DCGAN) prop  []\n",
      "Obj  Flexible_Discriminant_Analysis_(FDA) prop  []\n",
      "Obj  GoogleNet/Inception prop  []\n",
      "Obj  Image/Document_Clustering prop  []\n",
      "Obj  Indexing,_Retrieval_and_Unification prop  []\n",
      "Obj  Keypoint_Detection/Interest_Point_Detection prop  []\n",
      "Obj  Laplacian_Pyramid_GAN_(LAPGAN) prop  []\n",
      "Obj  Mixture_Discriminant_Analysis_(MDA) prop  []\n",
      "Obj  Multidimensional_Scaling_(MDS) prop  []\n",
      "Obj  Optical_Character_Recognition_(OCR) prop  []\n",
      "Obj  Partial_Least_Squares_Regression_(PLSR) prop  []\n",
      "Obj  Principal_Component_Regression_(PCR) prop  []\n",
      "Obj  Quadratic_Discriminant_Analysis_(QDA) prop  []\n",
      "Obj  RANdom_SAmple_Consensus(RANSAC) prop  []\n",
      "Obj  Super_Resolution_GAN_(SRGAN) prop  []\n",
      "Obj  Unsupervised_Pretrained_Networks_(UPN) prop  []\n"
     ]
    }
   ],
   "source": [
    "for obj in onto.classes():\n",
    "    print('Obj ', obj.name, 'prop ', list(obj.get_properties(obj)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Property:  Developer . Domain:  [] . Range:  []\n",
      "Property:  Documenter . Domain:  [] . Range:  []\n",
      "Property:  Domain . Domain:  [] . Range:  []\n",
      "Property:  activity . Domain:  [] . Range:  []\n",
      "Property:  agent . Domain:  [] . Range:  []\n",
      "Property:  alternateOf . Domain:  [.Speech-Recognition, .Speech_Recognition] . Range:  [.Fundamental]\n",
      "Property:  atLocation . Domain:  [] . Range:  []\n",
      "Property:  creator . Domain:  [] . Range:  []\n",
      "Property:  employsTechnique . Domain:  [] . Range:  []\n",
      "Property:  entity . Domain:  [] . Range:  []\n",
      "Property:  generalizes . Domain:  [] . Range:  []\n",
      "Property:  generated . Domain:  [] . Range:  []\n",
      "Property:  hadActivity . Domain:  [] . Range:  []\n",
      "Property:  hadGeneration . Domain:  [] . Range:  []\n",
      "Property:  hadMember . Domain:  [] . Range:  []\n",
      "Property:  hadRole . Domain:  [] . Range:  []\n",
      "Property:  hadUsage . Domain:  [] . Range:  []\n",
      "Property:  hasAlgorithmClass . Domain:  [] . Range:  []\n",
      "Property:  hasAlgorithmConfiguration . Domain:  [] . Range:  []\n",
      "Property:  hasFeature . Domain:  [] . Range:  []\n",
      "Property:  hasLearningMethod . Domain:  [] . Range:  []\n",
      "Property:  hasLearningProblem . Domain:  [] . Range:  []\n",
      "Property:  hasTarget . Domain:  [] . Range:  []\n",
      "Property:  hasTool . Domain:  [] . Range:  []\n",
      "Property:  homepage . Domain:  [] . Range:  []\n",
      "Property:  implements . Domain:  [] . Range:  []\n",
      "Property:  influenced . Domain:  [] . Range:  []\n",
      "Property:  influencer . Domain:  [] . Range:  []\n",
      "Property:  invalidated . Domain:  [] . Range:  []\n",
      "Property:  isAlgorithmConfigurationOf . Domain:  [] . Range:  []\n",
      "Property:  member . Domain:  [] . Range:  []\n",
      "Property:  memberOf . Domain:  [] . Range:  []\n",
      "Property:  publishedBy . Domain:  [] . Range:  []\n",
      "Property:  publishedIn . Domain:  [] . Range:  []\n",
      "Property:  range . Domain:  [] . Range:  []\n",
      "Property:  release . Domain:  [] . Range:  []\n",
      "Property:  repository . Domain:  [] . Range:  []\n",
      "Property:  revision . Domain:  [] . Range:  []\n",
      "Property:  solved_by . Domain:  [] . Range:  []\n",
      "Property:  solves . Domain:  [] . Range:  []\n",
      "Property:  subClassOf . Domain:  [] . Range:  []\n",
      "Property:  subPropertyOf . Domain:  [] . Range:  []\n",
      "Property:  used . Domain:  [] . Range:  []\n",
      "Property:  wasAssociatedWith . Domain:  [] . Range:  []\n",
      "Property:  wasAttributedTowasDerivedFrom . Domain:  [] . Range:  []\n",
      "Property:  wasGeneratedBy . Domain:  [] . Range:  []\n",
      "Property:  wasInfluencedBy . Domain:  [] . Range:  []\n"
     ]
    }
   ],
   "source": [
    "for prop in onto.object_properties():\n",
    "    print('Property: ', prop.name, '. Domain: ', prop.domain, '. Range: ', prop.range )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Property:  Developer\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  Documenter\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  Domain\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  activity\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  agent\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  alternateOf\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  atLocation\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  creator\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  employsTechnique\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  entity\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  generalizes\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  generated\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hadActivity\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hadGeneration\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hadMember\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hadRole\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hadUsage\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasAlgorithmClass\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasAlgorithmConfiguration\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasFeature\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasLearningMethod\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasLearningProblem\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasTarget\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  hasTool\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  homepage\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  implements\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  influenced\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  influencer\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  invalidated\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  isAlgorithmConfigurationOf\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  member\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  memberOf\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  publishedBy\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  publishedIn\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  range\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  release\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  repository\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  revision\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  solved_by\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  solves\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  subClassOf\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  subPropertyOf\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  used\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  wasAssociatedWith\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  wasAttributedTowasDerivedFrom\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  wasGeneratedBy\n",
      "[]\n",
      "\n",
      "\n",
      "Property:  wasInfluencedBy\n",
      "[]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for prop in onto.object_properties():\n",
    "    print('Property: ', prop.name)\n",
    "    print(onto.search(prop = \"*\"))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Ontology\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Absolute_Error_Loss http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Activation_Functions http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#AdaDelta http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Adagrad http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Adam http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Agriculture http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Ant_colony_optimization http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#ArSinH_Function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#ArcTan_Function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Architecture http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Artificial_Bee_Colony_Optimization http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Artificial_Fish_Swarm_Algorithm http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Artificial_Intelligence http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Artificial_Neural_Networks http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Arts_and_Entertainment http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Assistance http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Astronomy http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Automobiles http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Autonomous_Vehicles http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Backpropogation http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bacterial_Foraging_Optimization http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Banking http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Behavior_based_AI http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bent_identity_Function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Best_First_Search http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bidirectional_Search http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Big_Data http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Binary_Classification_Loss_Functions http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Binary_sigmoidal_function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Binary_step_function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bioinformatics http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Biology http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bipolar_sigmoidal_function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bipolar_step_function http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Blind_Search http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bots http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Breadth-first_search http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Class\n",
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      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#subClassOf http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topObjectProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#subPropertyOf http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topObjectProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#used http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasInfluencedBy\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasAssociatedWith http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasInfluencedBy\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasAttributedTowasDerivedFrom http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasInfluencedBy\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasGeneratedBy http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasInfluencedBy\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#wasInfluencedBy http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topObjectProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#alternateOf http://www.w3.org/2000/01/rdf-schema#domain http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Speech-Recognition\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#alternateOf http://www.w3.org/2000/01/rdf-schema#domain http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Speech_Recognition\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#alternateOf http://www.w3.org/2000/01/rdf-schema#range http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Fundamental\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#URL http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#atTime http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#created http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#datatype http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#description http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#language http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#name http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#operating-system http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#platform http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#programming-language http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#revision http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#value http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#year http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2002/07/owl#topDataProperty\n",
      ".owl#alternateOf http://www.semanticweb.org/ram/ontologies/2019/6/.owl#sharesDefinitionWith 207\n",
      "208 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.w3.org/2002/07/owl#Axiom\n",
      "208 http://www.w3.org/2002/07/owl#annotatedSource .owl#alternateOf\n",
      "208 http://www.w3.org/2002/07/owl#annotatedProperty http://www.semanticweb.org/ram/ontologies/2019/6/.owl#sharesDefinitionWith\n",
      "208 http://www.w3.org/2002/07/owl#annotatedTarget 207\n",
      "208 http://www.w3.org/2000/01/rdf-schema#comment .owl#Speech_Recognition\n",
      "http://www.semanticweb.org/ram/ontologies/2019/6/.owl#sharesDefinitionWith http://www.w3.org/2000/01/rdf-schema#subPropertyOf http://www.w3.org/2000/01/rdf-schema#seeAlso\n"
     ]
    }
   ],
   "source": [
    "graph = default_world.as_rdflib_graph()\n",
    "for s,p,o in graph:\n",
    "    print(s, p, o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop = set([p for s, p, o in graph])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#sharesDefinitionWith'),\n",
       " rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#first'),\n",
       " rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#rest'),\n",
       " rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#type'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#comment'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#domain'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#range'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#subClassOf'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#subPropertyOf'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#annotatedProperty'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#annotatedSource'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#annotatedTarget'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#disjointWith'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#equivalentClass'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#members')}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "subs = set([s for s, p, o in graph])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Optical_Flow'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Machine_Learning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Binary_RBM_with_Contrastive_Divergence'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#microscopy'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Soft_Clipping_Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Scene_Classification'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Active_Object_Localization'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Audio-Visual_Video_Captioning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Distributional_learning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Text-To-Speech_Synthesis'),\n",
       " rdflib.term.BNode('65'),\n",
       " rdflib.term.BNode('112'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Image-Based_Localization'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Laws’_Texture_Energy_Approach'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Digital_Image_Processing'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#name'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Deformable_Object_Manipulation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#definition'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Hopfield_Network'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#TanH__Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Satellite_Imaging/Satellite_Imagery'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Shape_recognition'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Activation_Functions'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Statistics'),\n",
       " rdflib.term.BNode('16'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Density_Estimation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Dense_Video_Captioning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Statistical_learning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Embedding_loss_functions'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Thermal_Infrared_Object_Tracking'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Education'),\n",
       " rdflib.term.BNode('44'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Huber_Loss'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Defocus_Estimation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Depth_Prediction_For_Outdoor_Scene'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Video_Classification'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Culture_and_Humanities'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Terminology_Extraction'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Arts'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Boltzmann_Machine'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Deep_learning_synthesis'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Photography'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Hill-Climbing_Search'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Hough_Transform'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Machine_Vision'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Depth-limited_search'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Keypoint_Localization'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Super_Resolution'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Gradient_Descent'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Dynamic_time_warping'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#3D_Instance_Segmentation'),\n",
       " rdflib.term.BNode('194'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Provide_consultations_and_Companion_robots'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#3D_Pose_Estimation'),\n",
       " rdflib.term.BNode('178'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#hasTool'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Local_Thresholding'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Proportional_Logic'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Boundary_Detection'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Deep_Learning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#SPSS'),\n",
       " rdflib.term.BNode('115'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#The_Approach_of_Gerig_and_Klein'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#CPlusPlus'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Binary_Shape_Analysis'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Steering_Control'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Terrain_Mapping'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Converssational_Emotion_Recognition'),\n",
       " rdflib.term.BNode('33'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Cross_Entropy_Loss'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Neologism'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Meteorology'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Group_Activity_Recognition'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Binary_step_function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Wasserstein_GAN'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Politics'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#MX-Net'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Statistical_Methods'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#description'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Knowledge_and_Reasoning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#scikit-learn'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#KAZE'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Unsupervised_Person_Re-Identification'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Differential_and_Semi-differential_Invariants'),\n",
       " rdflib.term.BNode('109'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Partial-Order_Planning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Local_Outlier_Factor'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Census'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Marketing'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#f-GAN'),\n",
       " rdflib.term.URIRef('.owl#alternateOf'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Stochastic_Gradient_Descent_Regression'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Design_treatment_plans'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Video_Quality_Assessment'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Manufacturing'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Contour_Detection'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Goal_based_Agents'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Migrating_Birds_Optimization'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Binary_Cross-Entropy_(BCE)'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Document_Embedding'),\n",
       " rdflib.term.BNode('32'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Auxiliary_Classifier_GAN'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Classification'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#DualGAN'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Clustering'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Distributional_Reinforcement_Learning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Image_Dehazing'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Inductive_Logic_Programming'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#LightGBM'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Graddient_Boosting'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Gaze_Estimation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Relationship_Extraction'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Income'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Mini-Max_Algorithm'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Multi-Layer_Perceptron'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Sketch_Recognition'),\n",
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       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Symbolic_based_AI'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Word_Embeddings'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#OracleDataMining'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Learning_from_Observations'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#activity'),\n",
       " rdflib.term.BNode('136'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Probabilistic_Programming'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Simultaneous_Localization_and_Mapping'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#AdaDelta'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Artificial_Bee_Colony_Optimization'),\n",
       " rdflib.term.BNode('55'),\n",
       " rdflib.term.BNode('61'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Domain_Generalization'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#ELKI'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#First-order_Logic'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Information_Theory'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#subPropertyOf'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Image_Comprehension'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#AHE'),\n",
       " rdflib.term.BNode('150'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#YALE'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Prolog'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#culture'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Word-based_morphology'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Pedestrian_Detection'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#GoogleNet/Inception'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Performance_Metrics'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Sequence_to_Sequence_Speecg_Recognition'),\n",
       " rdflib.term.BNode('87'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Spectrum_Cartography'),\n",
       " rdflib.term.BNode('94'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Bipolar_rectified_linear_unit_(BReLU)'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Games'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Homography_Estimation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#SVD'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Novel_View_Synthesis'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#SAP'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Transfer-Learning'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#atTime'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Jpeg_Compression_Artifact_Reduction'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Blind_Image_Deblurring'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Iterative_deepening_A*_search'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Octave'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Conditional_GAN_(CGAN)'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Unsupervised_Video_Object_Segmentation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#entity'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#memberOf'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#DiscoGAN'),\n",
       " rdflib.term.BNode('156'),\n",
       " rdflib.term.BNode('163'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Sentence_Segmentaion'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Morpheme-based_morphology'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#RCNN'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Image_Classification'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Harris_Scale_Affine_Invariant_method'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Hebbian_Learning'),\n",
       " rdflib.term.BNode('46'),\n",
       " rdflib.term.BNode('119'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#genes_prediction'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#XGBoost'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Semi-Supervised_Person_Re-Identification'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Iterative_deepening_search'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Manner_Of_Articulation_Detection'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Inverse_square_root_unit_(ISRU)'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Deep_RNN'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Hidden_Markov_Models_based'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#KNIME'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Unknown-Environment'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#hasTarget'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Deep_Adversarial_Networks'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#First_Order_Optimization_Algorithms'),\n",
       " rdflib.term.BNode('128'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Hidden_Markov_models'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Voice_Conversion'),\n",
       " rdflib.term.BNode('17'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#AutoML'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Mixture_Discriminant_Analysis_(MDA)'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Probability'),\n",
       " rdflib.term.BNode('130'),\n",
       " rdflib.term.BNode('160'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Facial_Recognition_and_Modelling'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Stereo_Matching'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Energy_Based_GAN'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Conditional_VAE'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Instance_Segmentation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Kernel_SVM'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Harris_Interest_point_operator'),\n",
       " rdflib.term.BNode('56'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Robustness_regression'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Material_Recognition'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Topic_Segmentation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Knowledge_based'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Conditional_Planning'),\n",
       " rdflib.term.BNode('154'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Distributional_Semantics'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Distant_Speech_Recognition'),\n",
       " ...}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Convolutional_Neural_Networks'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/.owl#Recurrent_Neural_Networks')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rdflib\n",
    "list(graph.subjects(predicate =  rdflib.term.URIRef('http://www.w3.org/2002/07/owl#equivalentClass')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1088"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(onto.classes())) + len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "obs = set([o for s, p, o in graph])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "575"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{rdflib.term.BNode('1'),\n",
       " rdflib.term.BNode('10'),\n",
       " rdflib.term.BNode('100'),\n",
       " rdflib.term.BNode('101'),\n",
       " rdflib.term.BNode('102'),\n",
       " rdflib.term.BNode('103'),\n",
       " rdflib.term.BNode('104'),\n",
       " rdflib.term.BNode('106'),\n",
       " rdflib.term.BNode('107'),\n",
       " rdflib.term.BNode('108'),\n",
       " rdflib.term.BNode('109'),\n",
       " rdflib.term.BNode('11'),\n",
       " rdflib.term.BNode('110'),\n",
       " rdflib.term.BNode('112'),\n",
       " rdflib.term.BNode('113'),\n",
       " rdflib.term.BNode('114'),\n",
       " rdflib.term.BNode('115'),\n",
       " rdflib.term.BNode('116'),\n",
       " rdflib.term.BNode('118'),\n",
       " rdflib.term.BNode('119'),\n",
       " rdflib.term.BNode('120'),\n",
       " rdflib.term.BNode('121'),\n",
       " rdflib.term.BNode('122'),\n",
       " rdflib.term.BNode('123'),\n",
       " rdflib.term.BNode('124'),\n",
       " rdflib.term.BNode('125'),\n",
       " rdflib.term.BNode('127'),\n",
       " rdflib.term.BNode('128'),\n",
       " rdflib.term.BNode('129'),\n",
       " rdflib.term.BNode('13'),\n",
       " rdflib.term.BNode('131'),\n",
       " rdflib.term.BNode('132'),\n",
       " rdflib.term.BNode('133'),\n",
       " rdflib.term.BNode('135'),\n",
       " rdflib.term.BNode('136'),\n",
       " rdflib.term.BNode('137'),\n",
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       " rdflib.term.BNode('14'),\n",
       " rdflib.term.BNode('140'),\n",
       " rdflib.term.BNode('141'),\n",
       " rdflib.term.BNode('142'),\n",
       " rdflib.term.BNode('143'),\n",
       " rdflib.term.BNode('144'),\n",
       " rdflib.term.BNode('145'),\n",
       " rdflib.term.BNode('147'),\n",
       " rdflib.term.BNode('148'),\n",
       " rdflib.term.BNode('149'),\n",
       " rdflib.term.BNode('15'),\n",
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       " rdflib.term.BNode('152'),\n",
       " rdflib.term.BNode('153'),\n",
       " rdflib.term.BNode('154'),\n",
       " rdflib.term.BNode('155'),\n",
       " rdflib.term.BNode('156'),\n",
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       " rdflib.term.BNode('158'),\n",
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       " rdflib.term.BNode('16'),\n",
       " rdflib.term.BNode('160'),\n",
       " rdflib.term.BNode('161'),\n",
       " rdflib.term.BNode('162'),\n",
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       " rdflib.term.BNode('164'),\n",
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       " rdflib.term.BNode('187'),\n",
       " rdflib.term.BNode('188'),\n",
       " rdflib.term.BNode('189'),\n",
       " rdflib.term.BNode('19'),\n",
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       " rdflib.term.BNode('191'),\n",
       " rdflib.term.BNode('192'),\n",
       " rdflib.term.BNode('193'),\n",
       " rdflib.term.BNode('194'),\n",
       " rdflib.term.BNode('195'),\n",
       " rdflib.term.BNode('196'),\n",
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       " rdflib.term.BNode('199'),\n",
       " rdflib.term.BNode('2'),\n",
       " rdflib.term.BNode('20'),\n",
       " rdflib.term.BNode('200'),\n",
       " rdflib.term.BNode('201'),\n",
       " rdflib.term.BNode('202'),\n",
       " rdflib.term.BNode('203'),\n",
       " rdflib.term.BNode('204'),\n",
       " rdflib.term.BNode('205'),\n",
       " rdflib.term.BNode('207'),\n",
       " rdflib.term.BNode('21'),\n",
       " rdflib.term.BNode('22'),\n",
       " rdflib.term.BNode('23'),\n",
       " rdflib.term.BNode('24'),\n",
       " rdflib.term.BNode('25'),\n",
       " rdflib.term.BNode('26'),\n",
       " rdflib.term.BNode('27'),\n",
       " rdflib.term.BNode('29'),\n",
       " rdflib.term.BNode('3'),\n",
       " rdflib.term.BNode('30'),\n",
       " rdflib.term.BNode('31'),\n",
       " rdflib.term.BNode('32'),\n",
       " rdflib.term.BNode('33'),\n",
       " rdflib.term.BNode('34'),\n",
       " rdflib.term.BNode('35'),\n",
       " rdflib.term.BNode('36'),\n",
       " rdflib.term.BNode('37'),\n",
       " rdflib.term.BNode('38'),\n",
       " rdflib.term.BNode('39'),\n",
       " rdflib.term.BNode('41'),\n",
       " rdflib.term.BNode('42'),\n",
       " rdflib.term.BNode('43'),\n",
       " rdflib.term.BNode('44'),\n",
       " rdflib.term.BNode('45'),\n",
       " rdflib.term.BNode('46'),\n",
       " rdflib.term.BNode('47'),\n",
       " rdflib.term.BNode('48'),\n",
       " rdflib.term.BNode('49'),\n",
       " rdflib.term.BNode('5'),\n",
       " rdflib.term.BNode('50'),\n",
       " rdflib.term.BNode('51'),\n",
       " rdflib.term.BNode('52'),\n",
       " rdflib.term.BNode('54'),\n",
       " rdflib.term.BNode('55'),\n",
       " rdflib.term.BNode('56'),\n",
       " rdflib.term.BNode('57'),\n",
       " rdflib.term.BNode('58'),\n",
       " rdflib.term.BNode('59'),\n",
       " rdflib.term.BNode('6'),\n",
       " rdflib.term.BNode('61'),\n",
       " rdflib.term.BNode('62'),\n",
       " rdflib.term.BNode('63'),\n",
       " rdflib.term.BNode('64'),\n",
       " rdflib.term.BNode('65'),\n",
       " rdflib.term.BNode('66'),\n",
       " rdflib.term.BNode('67'),\n",
       " rdflib.term.BNode('68'),\n",
       " rdflib.term.BNode('69'),\n",
       " rdflib.term.BNode('7'),\n",
       " rdflib.term.BNode('70'),\n",
       " rdflib.term.BNode('71'),\n",
       " rdflib.term.BNode('72'),\n",
       " rdflib.term.BNode('73'),\n",
       " rdflib.term.BNode('74'),\n",
       " rdflib.term.BNode('75'),\n",
       " rdflib.term.BNode('76'),\n",
       " rdflib.term.BNode('77'),\n",
       " rdflib.term.BNode('78'),\n",
       " rdflib.term.BNode('79'),\n",
       " rdflib.term.BNode('8'),\n",
       " rdflib.term.BNode('80'),\n",
       " rdflib.term.BNode('81'),\n",
       " rdflib.term.BNode('82'),\n",
       " rdflib.term.BNode('83'),\n",
       " rdflib.term.BNode('84'),\n",
       " rdflib.term.BNode('85'),\n",
       " rdflib.term.BNode('86'),\n",
       " rdflib.term.BNode('87'),\n",
       " rdflib.term.BNode('88'),\n",
       " rdflib.term.BNode('89'),\n",
       " rdflib.term.BNode('9'),\n",
       " rdflib.term.BNode('90'),\n",
       " rdflib.term.BNode('91'),\n",
       " rdflib.term.BNode('92'),\n",
       " rdflib.term.BNode('93'),\n",
       " rdflib.term.BNode('95'),\n",
       " rdflib.term.BNode('96'),\n",
       " rdflib.term.BNode('97'),\n",
       " rdflib.term.BNode('99'),\n",
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       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Sinusoid_Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#SoftExponential_Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#SoftPlus_Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Soft_Clipping_Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Softmax_Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Speech_processing'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Squared_Error_Loss'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Statistical_AI'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Statistics'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Stochastic_gradient_descent'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Survey'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Tabu_Search_Algorithm'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#TanH__Function'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Transportation'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Travelling_Salesman_Problem'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Uniform_cost_search'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Urban_Development'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Variance_based_Thresholding'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#Verbal_Assistant'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#environmental'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#imaging'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#neuro_imaging'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#neuroscience'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#quality'),\n",
       " rdflib.term.URIRef('http://www.semanticweb.org/ram/ontologies/2019/6/AI_18-11-2019#warning_systems'),\n",
       " rdflib.term.URIRef('http://www.w3.org/1999/02/22-rdf-syntax-ns#nil'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2000/01/rdf-schema#seeAlso'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#AllDisjointClasses'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#AnnotationProperty'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#Axiom'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#Class'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#DatatypeProperty'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#NamedIndividual'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#ObjectProperty'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#Ontology'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#topDataProperty'),\n",
       " rdflib.term.URIRef('http://www.w3.org/2002/07/owl#topObjectProperty')}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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