Docs update and version increase

README.md

@@ -1,8 +1,8 @@
 [![Downloads](https://static.pepy.tech/badge/ontolearn)](https://pepy.tech/project/ontolearn)
 [![Downloads](https://img.shields.io/pypi/dm/ontolearn)](https://pypi.org/project/ontolearn/)
 [![Coverage](https://img.shields.io/badge/coverage-86%25-green)](https://ontolearn-docs-dice-group.netlify.app/usage/09_further_resources#code-coverage)
-[![Pypi](https://img.shields.io/badge/pypi-0.8.1-blue)](https://pypi.org/project/ontolearn/0.8.1/)
-[![Docs](https://img.shields.io/badge/documentation-0.8.1-yellow)](https://ontolearn-docs-dice-group.netlify.app/usage/01_introduction)
+[![Pypi](https://img.shields.io/badge/pypi-0.9.0-blue)](https://pypi.org/project/ontolearn/0.9.0/)
+[![Docs](https://img.shields.io/badge/documentation-0.9.0-yellow)](https://ontolearn-docs-dice-group.netlify.app/usage/01_introduction)
 [![Python](https://img.shields.io/badge/python-3.10.13+-4584b6)](https://www.python.org/downloads/release/python-31013/)
  
 
@@ -18,6 +18,7 @@ $E^+$ and $E^-$, learning [OWL Class expression](https://www.w3.org/TR/owl2-synt
 $$\forall p \in E^+\ \mathcal{K} \models H(p) \wedge \forall n \in E^-\ \mathcal{K} \not \models H(n).$$
 
 To tackle this supervised learning problem, ontolearn offers many symbolic, neuro-symbolic and deep learning based Learning algorithms: 
+- **TDL** → Tree-based OWL Class Expression Learner for Large Graphs
 - **Drill** → [Neuro-Symbolic Class Expression Learning](https://www.ijcai.org/proceedings/2023/0403.pdf)
 - **EvoLearner** → [EvoLearner: Learning Description Logics with Evolutionary Algorithms](https://dl.acm.org/doi/abs/10.1145/3485447.3511925)
 - **NCES2** → [Neural Class Expression Synthesis in ALCHIQ(D)](https://papers.dice-research.org/2023/ECML_NCES2/NCES2_public.pdf)
@@ -121,7 +122,7 @@ print(owl_expression_to_sparql(expression=h))
 save_owl_class_expressions(expressions=h,path="#owl_prediction")
 ```
 
-Fore more please refer to  the [examples](https://github.com/dice-group/Ontolearn/tree/develop/examples) folder.
+Fore more please refer to the [examples](https://github.com/dice-group/Ontolearn/tree/develop/examples) folder.
 
 ## ontolearn-webservice 
 

docs/usage/01_introduction.md

@@ -1,46 +1,44 @@
 # About Ontolearn
 
-**Version:** ontolearn 0.8.1
+**Version:** ontolearn 0.9.0
 
 **GitHub repository:** [https://github.com/dice-group/Ontolearn](https://github.com/dice-group/Ontolearn)
 
 **Publisher and maintainer:** [DICE](https://dice-research.org/) - data science research group of [Paderborn University](https://www.uni-paderborn.de/en/university).
 
-**Contact**: [onto-learn@lists.uni-paderborn.de](mailto:onto-learn@lists.uni-paderborn.de)
+**Contact**: [cdemir@mail.uni-paderborn.de](mailto:cdemir@mail.uni-paderborn.de), [alkid@mail.uni-paderborn.de](mailto:alkid@mail.uni-paderborn.de)
 
 **License:** MIT License
 
 --------------------------------------------------------------------------------------------
 
-Ontolearn is an open-source software library for explainable structured machine learning in Python.
+OntoLearn is an open-source software library designed for explainable structured machine learning in OWL 2.0 ontologies.
+Our primary objective is to leverage structured learning techniques within the OWL framework, providing a robust and 
+interpretable approach to ontology-based machine learning.
 
-Ontolearn started with the goal of using _Explainable Structured Machine Learning_ 
-in OWL 2.0 ontologies and this
-exactly what our library offers. The main contribution are the exclusive concept learning
-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).
+One of OntoLearn’s key contributions is its exclusive concept learning algorithms, specifically tailored for Description 
+Logics (DL). The library currently includes nine fully functional algorithms capable of learning complex concepts in DL. 
+For further details and references, relevant research 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). _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.
-So now we use owlapy not only for OWL 2 entities representation but
-for ontology manipulation and reasoning as well.
+At the core of OntoLearn lies [Owlapy]((https://github.com/dice-group/owlapy)), a Python package inspired by the OWL API (its Java counterpart) and developed by 
+the OntoLearn team. To enhance modularity, readability, and maintainability, we have separated Owlapy from Ontolearn into an 
+independent repository. This modular approach allows Owlapy to serve not only as a framework for representing OWL 2 
+entities, but also as a tool for ontology manipulation and reasoning.
 
 ---------------------------------------
 
 **Ontolearn (including owlapy and ontosample) can do the following:**
 
-- Load/save ontologies in RDF/XML, OWL/XML.
+- **Use concept learning algorithms to generate hypotheses for classifying positive examples in a learning problem**.
+- **Use local datasets or datasets that are hosted on a triplestore server, for the learning task.**
+- Construct/Generate class expressions and evaluate them using different metrics.
+- Define learning problems.
+- Load/create/save ontologies in RDF/XML, OWL/XML.
 - Modify ontologies by adding/removing axioms.
 - Access individuals/classes/properties of an ontology (and a lot more).
-- Define learning problems.
-- Sample ontologies.
-- Construct class expressions.
-- Use concept learning algorithms to classify positive examples in a learning problem.
-- Use local datasets or datasets that are hosted on a triplestore server, for the learning task.
 - Reason over an ontology.
-- Other convenient functionalities like converting OWL class expressions to SPARQL or DL syntax.
+- Convenient functionalities like converting OWL class expressions to SPARQL or DL syntax.
+- Sample ontologies.
 
 ------------------------------------
 

docs/usage/02_installation.md

@@ -1,12 +1,12 @@
 # Installation
 
-Since Ontolearn is a Python library, you will need to have Python on
-your system. Python comes in various versions and with different,
-sometimes conflicting dependencies. Hence, most guides will recommend
-to set up a "virtual environment" to work in.
+Since Ontolearn is a Python library, you will need to have Python installed on
+your system (currently supporting version 3.10.13 or higher). Since python comes in various 
+versions and with different, sometimes conflicting dependencies, most guides will 
+recommend to set up a "virtual environment" to work in and so do we.
 
-One such system for virtual python environments is 
-[conda](https://conda.io/projects/conda/en/latest/index.html).
+To create a virtual python environments you can consider using the builtin python module
+[venv](https://docs.python.org/3/library/venv.html) or [conda](https://conda.io/projects/conda/en/latest/index.html). 
 
 ## Installation via _pip_
 
@@ -71,10 +71,8 @@ to successfully pass all the tests:
 ```shell
 pytest
 ```
-Note: Since Unix and Windows reference files differently, the test are set to work on Linux 
-but in Widows the filepaths throughout test cases should be changed which is something that
-is not very convenient to do. If you really want to run the tests in Windows, you can
-make use of the replace all functionality to change them.
+Note: The tests are designed to run successfully on Linux machines since we also use them in 
+GitHub Action. Therefore, trying to run them on a Windows machine can lead to some issues.
 
 ## Download External Files
 

docs/usage/04_knowledge_base.md

@@ -7,7 +7,7 @@ and a reasoner of type [AbstractOWLReasoner](https://dice-group.github.io/owlapy
 are not compatible with each other. For example, you can not use [TripleStore](ontolearn.triple_store.TripleStore) 
 knowledge base with 
 [StructuralReasoner](https://dice-group.github.io/owlapy/autoapi/owlapy/owl_reasoner/index.html#owlapy.owl_reasoner.StructuralReasoner), 
-but you can use _TripleStore_ KB with [TripleStoreReasoner](ontolearn.triple_store.TripleStoreReasoner).
+but you can use [TripleStore](ontolearn.triple_store.TripleStore) knowledge base with [TripleStoreReasoner](ontolearn.triple_store.TripleStoreReasoner).
 _AbstractKnowledgeBase_ contains the necessary methods to facilitate _Structured Machine Learning_.
 
 Currently, there are two implementation of _AbstractKnowledgeBase_:
@@ -20,9 +20,10 @@ Currently, there are two implementation of _AbstractKnowledgeBase_:
 These terms may be used interchangeably sometimes but in Ontolearn they are not the same thing,
 although they share a lot of similarities. An ontology in owlapy, as explained 
 [here](https://dice-group.github.io/owlapy/usage/ontologies.html) is the object where we load 
-the OWL 2.0 ontologies from a _.owl_ file containing the ontology in an RDF/XML or OWL/XML format.
-On the other side a knowledge base combines an ontology and a reasoner together.
-Therefore, differently from the ontology you can use methods that require reasoning. You can check 
+the OWL 2.0 ontologies (supporting different formats OWL/XML, RDF/XML, Triples etc.)
+On the other side a knowledge base combines an ontology and a reasoner together and is main purpose
+is to ease the process of concept learning serving as both a storing entity and a data retrieval entity.
+Therefore, differently from the ontology object you can use reasoning methods. You can check 
 the methods for each in the links below:
 
 - [AbstractKnowledgeBase](ontolearn.knowledge_base.AbstractKnowledgeBase)
@@ -37,16 +38,13 @@ is required to run a learning algorithm.
 can retrieve information from signature of this ontology. In case of a local the ontology, it can be modified and 
 saved.
 
-- Although they have some similar functionalities, there are a lot of other distinct 
-functionalities that each of them has.
-
-
+
 ## Create an Instance of KnowledgeBase
 
-Let us show how you can initialize an object of `KnowledgeBase`.
-We consider that you have already an OWL 2.0 ontology (containing *.owl* extension).
+Let us show how you can initialize an instance of `KnowledgeBase`.
+We consider that you have already an OWL 2.0 ontology locally (for example a file ending with *.owl*).
 
-The simplest way is to use the path of your _.owl_ file as follows:
+The simplest way is to use the path of your local ontology as follows:
 
 ```python 
 from ontolearn.knowledge_base import KnowledgeBase
@@ -55,7 +53,10 @@ kb = KnowledgeBase(path="file://KGs/Family/father.owl")
 ```
 
 What happens in the background is that the ontology located in this path will be loaded
-in the `AbstractOWLOntology` object of `kb` as done [here](https://dice-group.github.io/owlapy/usage/ontologies.html#loading-an-ontology).
+in the `AbstractOWLOntology` object of `kb` as well as a reasoner will be created using that
+ontology during initialisation. You may as well initialise an instance of `KnowledgeBase` using
+an instance of an ontology and reasoner. For this example we are using a minimalistic ontology
+called the _father_ ontology which you can download as instructed [here](02_installation.md#download-external-files).
 
 
 ## Ignore Concepts
@@ -145,8 +146,8 @@ male_individuals = kb.individuals(male_concept)
 Sometimes ontologies and therefore knowledge bases can get very large and our
 concept learners become inefficient in terms of runtime. Sampling is an approach
 to extract a portion of the whole knowledge base without changing its semantic and
-still being expressive enough to yield results with as little loss of quality as 
-possible. [OntoSample](https://github.com/alkidbaci/OntoSample/tree/main) is 
+still being expressive enough to yield results (in the learning task) with as little 
+loss of quality as possible. [OntoSample](https://github.com/alkidbaci/OntoSample/tree/main) is 
 a library that we use to perform the sampling process. It offers different sampling 
 techniques which fall into the following categories:
 
@@ -164,7 +165,10 @@ You can check them [here](https://github.com/alkidbaci/OntoSample/tree/main).
 
 When operated on its own, Ontosample uses a light version of Ontolearn (`ontolearn_light`) 
 to reason over ontologies, but when both packages are installed in the same environment 
-it will use `ontolearn` module instead. This is made for compatibility reasons.
+it will use `ontolearn` module instead. This is made for compatibility reasons. However, since
+the libraries are managed separately, you may encounter potential errors when installing them
+in the same environment. In this case we recommend using Ontosample in another environment 
+to perform sampling.
 
 Ontosample treats the knowledge base as a graph where nodes are individuals
 and edges are object properties. However, Ontosample also offers support for 
@@ -237,15 +241,17 @@ folder. You will find descriptive comments in that script that will help you und
 
 For more details about OntoSample you can see [this paper](https://dl.acm.org/doi/10.1145/3583780.3615158).
 
+Note: You cannot use sampling on a `TripleStore` knowledge base.
+
 ## TripleSore Knowledge Base
 
-Instead of going through nodes using expensive computation resources why not just make use of the
+Instead of querying knowledge graphs loaded locally using expensive computation resources why not just make use of the
 efficient approach of querying a triplestore using SPARQL queries. We have brought this 
-functionality to Ontolearn for our learning algorithms, and we take care of the conversion part behind the scene.
+functionality to Ontolearn for our learning algorithms.
 Let's see what it takes to make use of it.
 
 First of all you need a server which should host the triplestore for your ontology. If you don't
-already have one, see [Loading and Launching a Triplestore](#loading-and-launching-a-triplestore) below.
+already have one and just want to try things out, see [Loading and Launching a Triplestore](#loading-and-launching-a-triplestore) below.
 
 Now you can simply initialize an instance of `TripleStore` class that will serve as an input for your desired 
 concept learner: