Merging TripleStore and TripleStroreKnowledgeBase, Refactoring and Documentation update

.github/workflows/docs.yml

@@ -6,6 +6,8 @@ on:
      - master
      - documentation # only for quick updates or testing purposes
   pull_request:
+    branches:
+      - master
 
 jobs:
   docs:

docs/index.rst

@@ -11,6 +11,7 @@ Ontolearn is an open-source software library for explainable structured machine
    usage/02_installation
    usage/03_examples
    usage/04_knowledge_base
+   usage/05_evaluate_ce
    usage/06_concept_learners
    usage/09_further_resources
    autoapi/ontolearn/index

docs/usage/01_introduction.md

@@ -20,8 +20,7 @@ exactly what our library offers. The main contribution are the exclusive concept
 algorithms that are part of this library. Currently, we have 6 fully functioning algorithms that 
 learn concept in description logics. Papers can be found [here](09_further_resources.md).
 
-For the base (core) module of Ontolearn we use [owlapy](https://github.com/dice-group/owlapy)
-which on its end uses [Owlready2](https://owlready2.readthedocs.io/en/latest/index.html). _Owlapy_ is a python package
+For the base (core) module of Ontolearn we use [owlapy](https://github.com/dice-group/owlapy). _Owlapy_ is a python package
 based on owlapi (the java counterpart), and implemented by us, the Ontolearn team. 
 For the sake of modularization we have moved it in a separate repository.
 The modularization aspect helps us to increase readability and reduce complexity.
@@ -46,4 +45,4 @@ for ontology manipulation and reasoning as well.
 ------------------------------------
 
 The rest of content after "examples" is build as a top-to-bottom guide, but nevertheless self-containing, where
-you can learn more in depth about the capabilities of Ontolearn.
+you can learn more in depth about the components of Ontolearn.

docs/usage/02_installation.md

@@ -79,10 +79,9 @@ make use of the replace all functionality to change them.
 ## Download External Files
 
 Some resources like pre-calculated embeddings or `pre_trained_agents` and datasets (ontologies)
-are not included in the repository directly. Use the command line command `wget`
-to download them from our data server.
+are not included in the repository directly. Use the command `wget` to download them from our data server.
 
-> **NOTE: Before you run this commands in your terminal, make sure you are 
+> **NOTE: Before you run the following commands in your terminal, make sure you are 
 in the root directory of the project!**
 
 To download the datasets:
@@ -109,6 +108,14 @@ Finally, remove the _.zip_ file:
 rm KGs.zip
 ```
 
+To download learning problems:
+
+```shell
+wget https://files.dice-research.org/projects/Ontolearn/LPs.zip
+```
+
+Follow the same steps to unzip as the in the KGs case.
+
 --------------------------------------------------------
 
 ### NCES data: 
@@ -130,7 +137,7 @@ rm -f NCESData.zip
 
 ### CLIP data:
 
-```commandline
+```shell
 wget https://files.dice-research.org/projects/Ontolearn/CLIP/CLIPData.zip
 unzip CLIPData.zip
 rm CLIPData.zip 
@@ -143,11 +150,18 @@ it is necessary to use the `build` tool. It can be invoked with:
 
 ```shell
 python -m build
+
+# or
+
+python setup.py bdist_wheel sdist
 ```
 
-from the main source code folder. Packages created by `build` can then
-be uploaded as releases to the [Python Package Index (PyPI)](https://pypi.org/) using
-[twine](https://pypi.org/project/twine/).
+Distribution packages that are created, can then
+be published to the [Python Package Index (PyPI)](https://pypi.org/) using [twine](https://pypi.org/project/twine/).
+
+```shell
+py -m twine upload --repository pypi dist/*
+```
 
 
 ### Building the docs
@@ -167,12 +181,17 @@ sphinx-build -M latex docs/ docs/_build/
 
 ## Simple Linting
 
-Using the following command will run the linting tool [flake8](https://flake8.pycqa.org/) on the source code.
+You can lint check using [flake8](https://flake8.pycqa.org/):
 ```shell
 flake8
 ```
 
-Additionally, you can specify the path where you want to flake8 to run.
+or ruff:
+```shell
+ruff check
+```
+
+Additionally, you can specify the path where you want to execute the linter.
 
 
 ----------------------------------------------------------------------

docs/usage/03_examples.md

@@ -2,7 +2,7 @@
 
 In this guide we will show some non-trival examples of typical use-cases of Ontolearn
 which you can also find in the
-[examples](https://github.com/dice-group/Ontolearn/tree/develop/examples) folder.
+[examples](https://github.com/dice-group/Ontolearn/tree/master/examples) folder.
 
 
 ## Ex. 1: Learning Over a Local Ontology
@@ -133,7 +133,7 @@ save_owl_class_expressions(expressions=h, path="owl_prediction")
 Here we have used the triplestore endpoint as you see in step _(1)_ which is
 available only on a private network. However, you can host your own triplestore
 server following [this guide](06_concept_learners.md#loading-and-launching-a-triplestore)
-and run TDL using you own local endpoint.
+and run TDL using you own local endpoint. We have a [script](https://github.com/dice-group/Ontolearn/blob/master/examples/concept_learning_via_triplestore_example.py) for that also.
 
 --------------------------------------------------------------
 
@@ -263,6 +263,6 @@ if __name__ == '__main__':
 
 -----------------------------------------------------------
 
-In the next guide we will explore the [KnowledgeBase](ontolearn.knowledge_base.KnowledgeBase) class that is needed to 
+In the next guide we will explore the [KnowledgeBase](ontolearn.knowledge_base.KnowledgeBase) class which is needed to 
 run a concept learner.