Implemented triples and enitity linking using spacy

kg.py

@@ -0,0 +1,111 @@
+import PyPDF2
+import nltk
+import spacy
+nlp = spacy.load("en_core_web_sm")  
+nlp.add_pipe("entityLinker", last=True)
+import re
+from spacy.matcher import Matcher
+nltk.download('punkt')
+
+def extract_sentences_from_pdf(pdf_path):
+    text = ""
+    with open(pdf_path, 'rb') as file:
+        pdf_reader = PyPDF2.PdfReader(file)
+
+        for page_num in range(len(pdf_reader.pages)):
+            text += pdf_reader.pages[page_num].extract_text()
+
+    sentences = nltk.sent_tokenize(text)
+    return sentences
+
+def get_relation(sent):
+    doc = nlp(sent)
+
+    matcher = Matcher(nlp.vocab)
+
+    pattern = [{'DEP':'ROOT'},
+               {'DEP':'prep','OP':"?"},
+               {'DEP':'agent','OP':"?"},
+               {'POS':'ADJ','OP':"?"}]
+
+    matcher.add("matching_1", [pattern])
+
+    matches = matcher(doc)
+    k = len(matches) - 1
+
+    span = doc[matches[k][1]:matches[k][2]]
+
+    return span.text
+def extract_entities(sent):
+
+  try:
+
+    ent1 = ""
+    ent2 = ""
+    prv_tok_dep = ""   
+    prv_tok_text = ""   
+    prefix = ""
+    modifier = ""
+
+    for tok in nlp(sent):
+         if tok.dep_ != "punct":
+          if tok.dep_ == "compound":
+            prefix = tok.text
+            if prv_tok_dep == "compound":
+              prefix = prv_tok_text + " "+ tok.text
+
+
+          if tok.dep_.endswith("mod") == True:
+            modifier = tok.text
+
+            if prv_tok_dep == "compound":
+              modifier = prv_tok_text + " "+ tok.text
+
+          if tok.dep_.find("subj") == True:
+            ent1 = modifier +" "+ prefix + " "+ tok.text
+            prefix = ""
+            modifier = ""
+            prv_tok_dep = ""
+            prv_tok_text = ""
+
+
+          if tok.dep_.find("obj") == True:
+            ent2 = modifier +" "+ prefix +" "+ tok.text
+
+          prv_tok_dep = tok.dep_
+          prv_tok_text = tok.text
+
+    relation = get_relation(sentence)
+    doc = nlp(ent1.strip())
+    entity_linking1 = None
+    if doc._.linkedEntities and doc._.linkedEntities[0] is not None and doc._.linkedEntities[0].get_url() is not None:
+      entity_linking1 = doc._.linkedEntities[0].get_url()
+
+    if entity_linking1 is None:
+       entity_linking1 = 'Not Found'
+    doc2 = nlp(ent2.strip())
+    entity_linking2 = None
+    if doc2._.linkedEntities and doc2._.linkedEntities[0] is not None and doc2._.linkedEntities[0].get_url() is not None:
+      entity_linking2 = doc2._.linkedEntities[0].get_url()
+    if entity_linking2 is None:
+       entity_linking2 = 'Not found'
+    return [ent1.strip(),  relation, ent2.strip()], [entity_linking1,entity_linking2]
+  except NameError:
+      print('error with sent {sent}',sent)
+def remove_numeric_sentences(sentences):
+    cleaned_sentences = [re.sub(r'\n', '', sentence) for sentence in sentences if not re.match(r'^[\d\s.]+$', sentence)]
+    return cleaned_sentences
+
+pdf_path = "KG_BOOK_CHAPTER_3.pdf"
+sentences = extract_sentences_from_pdf(pdf_path)
+sentences = remove_numeric_sentences(sentences)
+triples = []
+entity_linkings= []
+
+
+for i, sentence in enumerate(sentences, 1):
+    triple, entity_linking = extract_entities(sentence)
+    triples.append(triple)
+    entity_linkings.append(entity_linking)
+print(triples)
+print(entity_linkings)