diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 07b5b5dc..4b8019da 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -1,56 +1,63 @@
-#name: Build docs
-#
-#on:
-#  push:
-#    branches:
-#     - master
-#     - develop
-#  pull_request:
-#
-#jobs:
-#  docs:
-#    runs-on: ubuntu-latest
-#    strategy:
-#      max-parallel: 5
-#
-#    steps:
-#    - uses: actions/checkout@v3
-#    - name: Install tox
-#      id: install-tox
-#      run: |
-#        pip install tox
-#        echo "tox_version=$(pip list | grep tox | tr -d ' ')" >> $GITHUB_OUTPUT
-#    - name: prepare required software
-#      run: |
-#        # epstopdf & dot & noto-fonts
-#        sudo apt update && sudo apt install texlive-font-utils graphviz fonts-noto
-#    - uses: actions/cache@v3
-#      with:
-#        key: ${{ runner.os }}-${{ steps.install-tox.outputs.tox_version }}-docs-${{ hashFiles('environment.yml', 'tox.ini') }}
-#        path: .tox
-#    - name: Build docs with tox
-#      run: |
-#        tox -o -e docs
-#    - name: Build latex docs with tox
-#      run: |
-#        tox -o -e docs -- latex
-#    - name: Compile LaTeX document
-#      uses: docker://texlive/texlive:latest
-#      with:
-#        args: make -C docs/_build/latex
-#    - run: |
-#        cp docs/_build/latex/ontolearn.pdf docs/_build/html/
-#    - name: Deploy to netlify
-#      uses: nwtgck/actions-netlify@v1.2
-#      with:
-#        publish-dir: 'docs/_build/html'
-#        production-branch: develop
-#        github-token: ${{ secrets.GITHUB_TOKEN }}
-#        deploy-message: "Deploy from GitHub Actions ${{ github.sha }}"
-#        alias: ${{ github.head_ref }}
-#        enable-pull-request-comment: false
-#        enable-commit-comment: false
-#      env:
-#        NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
-#        NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
-#      timeout-minutes: 5
+name: Build docs
+
+on:
+  push:
+    branches:
+     - master
+     - develop
+  pull_request:
+
+jobs:
+  docs:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [ "3.9.18" ]
+      max-parallel: 5
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt   
+
+    - name: Prepare required software
+      run: |
+        # epstopdf & dot & noto-fonts
+        sudo apt update && sudo apt install texlive-font-utils graphviz fonts-noto\
+
+    - name: Build docs
+      run: |
+        sphinx-build -M html docs/ docs/_build/
+
+    - name: Build LaTeX docs
+      run: |
+        sphinx-build -M latex docs/ docs/_build/
+
+    - name: Compile LaTeX document
+      uses: docker://texlive/texlive:latest
+      with:
+        args: make -C docs/_build/latex
+    - run: |
+        cp docs/_build/latex/ontolearn.pdf docs/_build/html/
+
+    - name: Deploy to netlify
+      uses: nwtgck/actions-netlify@v1.2
+      with:
+        publish-dir: 'docs/_build/html'
+        production-branch: develop
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        deploy-message: "Deploy from GitHub Actions ${{ github.sha }}"
+        alias: ${{ github.head_ref }}
+        enable-pull-request-comment: false
+        enable-commit-comment: false
+      env:
+        NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
+        NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
+      timeout-minutes: 5
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 85607ffd..34ce4ce8 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -15,11 +15,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      # - name: Set up Conda
-      #   uses: conda-incubator/setup-miniconda@v2
-      #   with:
-      #    environment-file: environment.yml
-        # conda run -n test pytest -p no:warnings -x
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
diff --git a/.mailmap b/.mailmap
deleted file mode 100644
index a9e96e92..00000000
--- a/.mailmap
+++ /dev/null
@@ -1 +0,0 @@
-Caglar Demir <caglardemir8@gmail.com>
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index 9312c357..00000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1,11 +0,0 @@
-include LICENSE
-include tox.ini
-include *.conf
-recursive-include KGs *.owl
-recursive-include KGs *.ttl
-recursive-include examples *.py
-recursive-include examples *.json
-recursive-include examples *.conf
-recursive-include tests *.py
-prune *.egg-info
-prune .tox
diff --git a/README.md b/README.md
index 71c15d84..9a3bbed7 100644
--- a/README.md
+++ b/README.md
@@ -21,8 +21,8 @@ pip install ontolearn
 or
 ```shell
 git clone https://github.com/dice-group/Ontolearn.git 
-conda create --name onto python=3.9.18 && conda activate onto && pip3 install -e . && python -c "import ontolearn"
-# To download knowledge graphs
+python -m venv venv && source venv/bin/activate # for Windows use: .\venv\Scripts\activate 
+pip install -r requirements.txt
 wget https://files.dice-research.org/projects/Ontolearn/KGs.zip -O ./KGs.zip && unzip KGs.zip
 ```
 
@@ -153,10 +153,11 @@ python -c 'import pandas as pd; print(pd.read_csv("carcinogenesis_results.csv",
 ## Deployment 
 
 ```shell
-pip install gradio
+pip install gradio # (check `pip show gradio` first)
 ```
 
-To deploy **EvoLearner** on the **Family** knowledge graph. Available models to deploy: **EvoLearner**, **NCES**, **CELOE** and **OCEL**.
+Available models to deploy: **EvoLearner**, **NCES**, **CELOE** and **OCEL**.
+To deploy **EvoLearner** on the **Family** knowledge graph:
 ```shell
 python deploy_cl.py --model evolearner --path_knowledge_base KGs/Family/family-benchmark_rich_background.owl
 ```
diff --git a/docs/api.rst b/docs/api.rst
deleted file mode 100644
index 2f7a5926..00000000
--- a/docs/api.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-Ontolearn API
-=============
-
-.. autosummary::
-   :toctree: source
-   :recursive:
-
-   ontolearn
-   owlapy
diff --git a/docs/conf.py b/docs/conf.py
index a9d7c52d..1362c51a 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -10,10 +10,9 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
-
+import os
+import sys
+sys.path.insert(0, os.path.abspath('..'))
 
 # -- Project information -----------------------------------------------------
 
@@ -27,11 +26,8 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinxext_autox',
+    'autoapi.extension',
     'sphinx.ext.githubpages',
-    # 'sphinx.ext.intersphinx',
-    # 'sphinx_automodapi.smart_resolver',
-    # 'sphinx.ext.inheritance_diagram',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
     'sphinxcontrib.plantuml',
@@ -39,6 +35,16 @@
     'sphinx_rtd_theme',
 ]
 
+# autoapi for ontolearn and owlapy. for owlapy we need to refer to its path in GitHub Action environment
+autoapi_dirs = ['../ontolearn', '/opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages/owlapy']
+
+# by default all are included but had to reinitialize this to remove private members from shoing
+autoapi_options = ['members', 'undoc-members', 'show-inheritance', 'show-module-summary', 'special-members',
+                   'imported-members']
+# this is set to false, so we can add it manually in index.rst together with the other .md files of the documentation.
+autoapi_add_toctree_entry = False
+
+
 inheritance_graph_attrs = dict(rankdir="TB")
 
 myst_enable_extensions = [
diff --git a/docs/index.rst b/docs/index.rst
index 6d43e799..cfb56ad2 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,7 +16,8 @@ Ontolearn is an open-source software library for explainable structured machine
    usage/07_reasoning_details
    usage/08_model_adapter
    usage/09_further_resources
-   api
+   autoapi/ontolearn/index
+   autoapi/owlapy/index
 
 
 .. raw:: latex
diff --git a/docs/usage/01_introduction.md b/docs/usage/01_introduction.md
index 48473609..85b380ff 100644
--- a/docs/usage/01_introduction.md
+++ b/docs/usage/01_introduction.md
@@ -1,6 +1,6 @@
 # Ontolearn
 
-**Version:** ontolearn 0.6.0
+**Version:** ontolearn 0.6.2
 
 **GitHub repository:** [https://github.com/dice-group/Ontolearn](https://github.com/dice-group/Ontolearn)
 
diff --git a/docs/usage/02_installation.md b/docs/usage/02_installation.md
index 3abbb048..e1cbb82e 100644
--- a/docs/usage/02_installation.md
+++ b/docs/usage/02_installation.md
@@ -5,19 +5,15 @@ 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.
 
-One such system for virtual python environments is
-[Anaconda](https://www.anaconda.com/). You can download miniconda from
-<https://docs.conda.io/en/latest/miniconda.html>.
+One such system for virtual python environments is python
+[venv](https://docs.python.org/3/library/venv.html). Since the command comes 
+together with python, you don't need to install any external tool.
 
-We have good experience with it and make use of conda in the
-[Installation from source](#installation-from-source) step.
 
 ## Installation via _pip_
 
 Released versions of Ontolearn can be installed using `pip`, the
-Package Installer for Python. It comes as part of Python. Please
-research externally (or use `conda create` command below) on how to
-create virtual environments for Python programs.
+Package Installer for Python. `pip` comes as part of Python.
 
 ```shell
 pip install ontolearn
@@ -30,39 +26,36 @@ and all its dependencies from <https://pypi.org/project/ontolearn/>.
 
 To download the Ontolearn source code, you will also need to have a
 copy of the [Git](https://git-scm.com/) version control system.
-If you haven't, you might also need to install java and curl:
+
+Install java and curl:
 ```shell
+# for Unix systems (Linux and macOS)
 sudo apt install openjdk-11-jdk
 sudo apt install curl
+# for Windows please check online for yourself :)
 ```
 
+Once you have the done previous step, you can continue setting up a virtual
+environment and installing the dependencies. You may as well use your IDE interface
+to set up your venv.
 
-Once you have `conda` and `git` installed, the following commands
-should be typed in your shell in order to download the Ontolearn
-development sources, install the dependencies listened in the
-`environment.yml` into a conda environment and create the necessary
-installation links to get started with the library.
-
-* Download (clone) the source code
+* ->First download (clone) the source code
   ```shell
   git clone https://github.com/dice-group/Ontolearn.git
   cd Ontolearn
   ```
   
-* Create a conda environment using the `environment.yml` file.
-  ```shell
-  conda env create -f environment.yml
-  conda activate ontolearn
-  ```
-* Install the development links so that Python will find the library
+* ->Create a python virtual environment. (We are not using conda anymore)
   ```shell
-  python -c 'from setuptools import setup; setup()' develop 
+  python -m venv venv 
+  source venv/bin/activate # --> for Unix systems
+  # .\venv\Scripts\activate  --> for Windows
   ```
-* Instead of the previous step there is also Possibility B, which is valid temporarily only in your current shell:
+* ->Install the dependencies
   ```shell
-  export PYTHONPATH=$PWD
+  pip install -r requirements.txt
   ```
-
+  
 Now you are ready to develop on Ontolearn or use the library!
 
 ### Verify installation
@@ -82,12 +75,16 @@ 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.
 
 ## 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.
+to download them from our data server.
 
 > **NOTE: Before you run this commands in your terminal, make sure you are 
 in the root directory of the project!**
@@ -133,10 +130,11 @@ rm -f NCESData.zip
 
 ## Building (sdist and bdist_wheel)
 
-In order to create a *distribution* of the Ontolearn source code, typically when creating a new release, it is necessary to use the `build` tool. It can be invoked with:
+In order to create a *distribution* of the Ontolearn source code, typically when creating a new release, 
+it is necessary to use the `build` tool. It can be invoked with:
 
 ```shell
-tox -e build
+python -m build
 ```
 
 from the main source code folder. Packages created by `build` can then
@@ -149,14 +147,14 @@ be uploaded as releases to the [Python Package Index (PyPI)](https://pypi.org/)
 The documentation can be built with
 
 ```shell
-tox -e docs
+sphinx-build -M html docs/ docs/_build/
 ```
 
 It is also possible to create a PDF manual, but that requires LaTeX to
 be installed:
 
 ```shell
-tox -e docs latexpdf
+sphinx-build -M latex docs/ docs/_build/
 ```
 
 ## Simple Linting
diff --git a/docs/usage/03_ontologies.md b/docs/usage/03_ontologies.md
index 713c0e1c..b827de77 100644
--- a/docs/usage/03_ontologies.md
+++ b/docs/usage/03_ontologies.md
@@ -25,7 +25,7 @@ are six persons (individuals), of which four are male and two are female.
 ## Loading an Ontology
 
 To load an ontology as well as to manage it, you will need an 
-[OWLOntologyManager](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3245)
+[OWLOntologyManager](owlapy.model.OWLOntologyManager)
 (this is an abstract class, concrete implementation in Ontolearn is mentioned below).
 An ontology can be loaded using the following Python code:
 
@@ -59,7 +59,7 @@ You can get the object properties in the signature:
 onto.object_properties_in_signature()
 ```
 
-For more methods, see the owlapy abstract class [OWLOntology](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3064)
+For more methods, see the owlapy abstract class [OWLOntology](owlapy.model.OWLOntology)
 or the concrete implementation in Ontolearn [OWLOntology_Owlready2](ontolearn.base.OWLOntology_Owlready2).
 
 ## Modifying an Ontology
@@ -73,12 +73,12 @@ allowing the ontology to evolve and adapt as new knowledge is gained.
 In owlapy we also have different axioms represented by different classes. You can check all
 the axioms classes [here](https://github.com/dice-group/owlapy/blob/main/owlapy/model/__init__.py). Some frequently used axioms are:
 
-- [OWLDeclarationAxiom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L2027)
-- [OWLObjectPropertyAssertionAxiom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L2817)
-- [OWLDataPropertyAssertionAxiom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L2835)
-- [OWLClassAssertionAxiom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L2447)
-- [OWLSubClassOfAxiom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L2368)
-- [OWLEquivalentClassesAxiom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L2183)
+- [OWLDeclarationAxiom](owlapy.model.OWLDeclarationAxiom)
+- [OWLObjectPropertyAssertionAxiom](owlapy.model.OWLObjectPropertyAssertionAxiom)
+- [OWLDataPropertyAssertionAxiom](owlapy.model.OWLDataPropertyAssertionAxiom)
+- [OWLClassAssertionAxiom](owlapy.model.OWLClassAssertionAxiom)
+- [OWLSubClassOfAxiom](owlapy.model.OWLSubClassOfAxiom)
+- [OWLEquivalentClassesAxiom](owlapy.model.OWLEquivalentClassesAxiom)
 
 
 #### Add a new Class
@@ -99,21 +99,21 @@ child_class_declaration_axiom = OWLDeclarationAxiom(child_class)
 manager.add_axiom(onto, child_class_declaration_axiom)
 ```
 In this example, we added the class 'child' to the father.owl ontology.
-Firstly we create an instance of [OWLClass](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L209) to represent the concept 
-of 'child' by using an [IRI](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/_iri.py#L47). 
+Firstly we create an instance of [OWLClass](owlapy.model.OWLClass) to represent the concept 
+of 'child' by using an [IRI](owlapy.model.IRI). 
 On the other side, an instance of `IRI` is created by passing two arguments which are
 the namespace of the ontology and the remainder 'child'. To declare this new class we need
 an axiom of type `OWLDeclarationAxiom`. We simply pass the `child_class` to create an 
 instance of this axiom. The final step is to add this axiom to the ontology using the 
-[OWLOntologyManager](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3245). We use the `add_axiom` method
+[OWLOntologyManager](owlapy.model.OWLOntologyManager). We use the `add_axiom` method
 of the `manager` to add into the ontology
 `onto` the axiom `child_class_declaration_axiom`.
 
 #### Add a new Object Property / Data Property
 
 The idea is the same as adding a new class. Instead of `OWLClass`, for object properties,
-you can use the class [OWLObjectProperty](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L380C7-L380C24) and for data
-properties you can use the class [OWLDataProperty](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L356C11-L356C11).
+you can use the class [OWLObjectProperty](owlapy.model.OWLObjectProperty) and for data
+properties you can use the class [OWLDataProperty](owlapy.model.OWLDataProperty).
 
 <!--pytest-codeblocks:cont-->
 
@@ -132,7 +132,7 @@ hasAge_dp_declaration_axiom = OWLDeclarationAxiom(hasAge_dp)
 manager.add_axiom(onto, hasAge_dp_declaration_axiom)
 ```
 
-See the [owlapy](https://github.com/dice-group/owlapy/blob/main/owlapy/model/__init__.py) repository for more OWL entities that you can add as a declaration axiom.
+See the [owlapy](owlapy) for more OWL entities that you can add as a declaration axiom.
 
 #### Add an Assertion Axiom
 
@@ -156,7 +156,7 @@ want to assert a class axiom for the individual `heinz`.
 We have used the class `OWLClassAssertionAxiom`
 where the first argument is the 'individual' `heinz` and the second argument is 
 the 'class_expression'. As the class expression, we used the previously defined class 
-`child_Class`. Finally, add the axiom by using `add_axiom` method of the [OWLOntologyManager](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3245).
+`child_Class`. Finally, add the axiom by using `add_axiom` method of the [OWLOntologyManager](owlapy.model.OWLOntologyManager).
 
 Let's show one more example using a `OWLDataPropertyAssertionAxiom` to assign the age of 17 to
 heinz. 
@@ -173,14 +173,14 @@ dp_assertion_axiom = OWLDataPropertyAssertionAxiom(heinz, hasAge_dp, literal_17)
 manager.add_axiom(onto, dp_assertion_axiom)
 ```
 
-[OWLLiteral](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L1128) is a class that represents the literal values in
+[OWLLiteral](owlapy.model.OWLLiteral) is a class that represents the literal values in
 Owlapy. We have stored the integer literal value of '18' in the variable `literal_17`.
 Then we construct the `OWLDataPropertyAssertionAxiom` by passing as the first argument, the 
 individual `heinz`, as the second argument the data property `hasAge_dp`, and the third 
 argument the literal value `literal_17`. Finally, add it to the ontology by using `add_axiom` 
 method.
 
-Check the [owlapy](https://github.com/dice-group/owlapy/blob/main/owlapy/model/__init__.py) repository to see all the OWL 
+Check the [owlapy](owlapy) to see all the OWL 
 assertion axioms that you can use.
 
 
@@ -199,7 +199,7 @@ argument is the axiom you want to remove.
 ## Save an Ontology
 
 If you modified an ontology, you may want to save it as a new file. To do this
-you can use the `save_ontology` method of the [OWLOntologyManager](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3245).
+you can use the `save_ontology` method of the [OWLOntologyManager](owlapy.model.OWLOntologyManager).
 It requires two arguments, the first is the ontology you want to save and The second
 is the IRI of the new ontology.
 
diff --git a/docs/usage/04_knowledge_base.md b/docs/usage/04_knowledge_base.md
index 94b3474b..fcff1b81 100644
--- a/docs/usage/04_knowledge_base.md
+++ b/docs/usage/04_knowledge_base.md
@@ -2,8 +2,8 @@
 
 In Ontolearn we represent a knowledge base 
 by the class [KnowledgeBase](ontolearn.knowledge_base.KnowledgeBase) which contains two main class attributes, 
-an ontology [OWLOntology](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3064)
-and a reasoner [OWLReasoner](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3322).
+an ontology [OWLOntology](owlapy.model.OWLOntology)
+and a reasoner [OWLReasoner](owlapy.model.OWLReasoner).
 It also contains the class and properties hierarchy as well as other
 Ontology-related attributes required for the Structured Machine Learning library.
 
@@ -19,7 +19,7 @@ differently from the ontology you can use methods that require reasoning. You ca
 the methods for each in the links below:
 
 - [KnowledgeBase](ontolearn.knowledge_base.KnowledgeBase)
-- [OWLOntology](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3064)
+- [OWLOntology](owlapy.model.OWLOntology)
 
 In summary:
 
@@ -80,7 +80,7 @@ concepts_to_ignore = {father_concept}  # you can add more than 1
 
 new_kb = kb.ignore_and_copy(ignored_classes=concepts_to_ignore)
 ```
-In this example, we have created an instance of [OWLClass](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L209) by using an [IRI](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/_iri.py#L47). 
+In this example, we have created an instance of [OWLClass](owlapy.model.OWLClass) by using an [IRI](owlapy.model.IRI). 
 On the other side, an instance of `IRI` is created by passing two parameters which are
 the namespace of the ontology and the remainder 'Father'.
 
@@ -90,7 +90,7 @@ You may need to work with individuals of a knowledge base.
 We cover different ways of accessing them.
 
 Let us give a  simple example of how to get the individuals that
-are classified by an [OWLClassExpression](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L209Expression). As a class expression, we will simply use the 
+are classified by an [OWLClassExpression](owlapy.model.OWLClassExpression). As a class expression, we will simply use the 
 concept 'male'.
 
 <!--pytest-codeblocks:cont-->
@@ -143,7 +143,7 @@ When using a concept learner, the generated concepts (class expressions) for a c
 need to be evaluated to see the performance. 
 To do that you can use the method `evaluate_concept` of `KnowledgeBase`. It requires the following arguments:
 
-1. a concept to evaluate: [OWLClassExpression](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L209Expression)
+1. a concept to evaluate: [OWLClassExpression](owlapy.model.OWLClassExpression)
 2. a quality metric: [AbstractScorer](ontolearn.abstracts.AbstractScorer)
 3. the encoded learning problem: [EncodedLearningProblem](ontolearn.learning_problem.EncodedPosNegLPStandard)
 
@@ -209,11 +209,11 @@ concept_to_test = OWLObjectIntersectionOf([not_female, exist_has_child_T])
 
 `kb` has an instance of [ConceptGenerator](ontolearn.concept_generator.ConceptGenerator)
 which we use in this case to create the negated concept `¬female`. The other classes 
-[OWLObjectProperty](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L380C7-L380C24), 
-[OWLObjectSomeValuesFrom](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L554) 
-and [OWLObjectIntersectionOf](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L650) are classes
+[OWLObjectProperty](owlapy.model.OWLObjectProperty), 
+[OWLObjectSomeValuesFrom](owlapy.model.OWLObjectSomeValuesFrom) 
+and [OWLObjectIntersectionOf](owlapy.model.OWLObjectIntersectionOf) are classes
 that represent different kind of axioms in owlapy and can be found in 
-[owlapy model](https://github.com/dice-group/owlapy/blob/main/owlapy/model/__init__.py) module. There are more kind of axioms there which you
+[owlapy model](owlapy.model) module. There are more kind of axioms there which you
 can use to construct class expressions like we did in the example above.
 
 ### Evaluation and results
diff --git a/docs/usage/05_reasoner.md b/docs/usage/05_reasoner.md
index 85c3b334..e1f288a2 100644
--- a/docs/usage/05_reasoner.md
+++ b/docs/usage/05_reasoner.md
@@ -31,7 +31,7 @@ from. Currently, there are the following reasoners available:
    structural_reasoner = OWLReasoner_Owlready2(onto)
    ```
 
-    The structural reasoner requires an ontology ([OWLOntology](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3064)).
+    The structural reasoner requires an ontology ([OWLOntology](owlapy.model.OWLOntology)).
   The second argument is `isolate` argument which isolates the world (therefore the ontology) where the reasoner is
   performing the reasoning. More on that on _[Reasoning Details](07_reasoning_details.md#isolated-world)_.
   The remaining argument, `triplestore_address`, is used in case you want to
@@ -90,7 +90,7 @@ from. Currently, there are the following reasoners available:
 ## Usage of the Reasoner
 All the reasoners available in the Ontolearn library inherit from the
 class: [OWLReasonerEx](ontolearn.base.ext.OWLReasonerEx). This class provides some 
-extra convenient methods compared to its base class [OWLReasoner](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3322), which is an 
+extra convenient methods compared to its base class [OWLReasoner](owlapy.model.OWLReasoner), which is an 
 abstract class.
 Further in this guide, we use 
 [OWLReasoner_Owlready2_ComplexCEInstances](ontolearn.base.OWLReasoner_Owlready2_ComplexCEInstances).
@@ -103,7 +103,7 @@ you can find an overview of it [here](03_ontologies.md).
 
 ## Class Reasoning
 
-Using an [OWLOntology](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L3064) you can list all the classes in the signature, 
+Using an [OWLOntology](owlapy.model.OWLOntology) you can list all the classes in the signature, 
 but a reasoner can give you more than that. You can get the subclasses, superclasses or the 
 equivalent classes of a class in the ontology:
 
@@ -121,7 +121,7 @@ male_sub_classes = ccei_reasoner.sub_classes(male)
 male_equivalent_classes = ccei_reasoner.equivalent_classes(male)
 ```
 
-We define the _male_ class by creating an [OWLClass](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L209) object. The 
+We define the _male_ class by creating an [OWLClass](owlapy.model.OWLClass) object. The 
 methods `super_classes` and `sub_classes` have 2 more boolean arguments: `direct` and `only_named`. 
 If `direct=True` then only the direct classes in the 
 hierarchy will be returned, else it will return every class in the hierarchy depending 
@@ -179,14 +179,14 @@ are more than 1, and we use the reasoner
 to get the values for each object property `op` of the individual `anna`. The values 
 are individuals which we store in the variable `object_properties_values` and are 
 printed in the end. The method `object_property_values` requires as the
-first argument, an [OWLNamedIndividual](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L917) that is the subject of the object property values and 
-the second argument an [OWLObjectProperty](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L380C7-L380C24) whose values are to be retrieved for the 
+first argument, an [OWLNamedIndividual](owlapy.model.OWLNamedIndividual) that is the subject of the object property values and 
+the second argument an [OWLObjectProperty](owlapy.model.OWLObjectProperty) whose values are to be retrieved for the 
 specified individual.  
 
 > **NOTE:** You can as well get all the data properties of an individual in the same way by using 
 `ind_data_properties` instead of `ind_object_properties` and `data_property_values` instead of 
 `object_property_values`. Keep in mind that `data_property_values` returns literal values 
-(type of [OWLLiteral](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L1128)).
+(type of [OWLLiteral](owlapy.model.OWLLiteral)).
 
 In the same way as with classes, you can also get the sub object properties or equivalent object properties.
 
@@ -218,7 +218,7 @@ hasChild_ranges = ccei_reasoner.object_property_ranges(hasChild)
 The method `instances` is a very convenient method. It takes only 1 argument that is basically
 a class expression and returns all the individuals belonging to that class expression. In Owlapy 
 we have implemented a Python class for each type of class expression.
-The argument is of type [OWLClassExpression](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L209Expression).
+The argument is of type [OWLClassExpression](owlapy.model.OWLClassExpression).
 
 Let us now show a simple example by finding the instances of the class _male_ and printing them:
 
diff --git a/docs/usage/06_concept_learners.md b/docs/usage/06_concept_learners.md
index 6abee13b..187018f4 100644
--- a/docs/usage/06_concept_learners.md
+++ b/docs/usage/06_concept_learners.md
@@ -149,8 +149,8 @@ typed_neg = set(map(OWLNamedIndividual, map(IRI.create, n)))
 lp = PosNegLPStandard(pos=typed_pos, neg=typed_neg)
 ```
 
-To construct an [OWLNamedIndividual](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/__init__.py#L917) 
-object an [IRI](https://github.com/dice-group/owlapy/blob/6a6338665a6df0845e67eda577327ca4c62f446b/owlapy/model/_iri.py#L47) is required as an input. 
+To construct an [OWLNamedIndividual](owlapy.model.OWLNamedIndividual) 
+object an [IRI](owlapy.model.IRI) is required as an input. 
 You can simply create an `IRI` object by calling the static method `create` 
 and passing the IRI as a `string`.
 
diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index d06b459e..00000000
--- a/environment.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-name: ontolearn
-channels:
-  - defaults
-  - conda-forge
-  - pytorch
-dependencies:
-  - python=3.9.18
-  - matplotlib=3.3.4
-  - pandas=1.2.3
-  - pytorch=1.8.0
-  - rdflib=6.0.2
-  - scikit-learn=1.0.2
-  - sortedcontainers=2.4.0
-  - flask=1.1.2
-  - deap=1.3.1
-  - httpx=0.21.1
-  - parsimonious=0.8.1
-  - tqdm=4.64.0
-  # testing
-  - tox=3.24.3
-  - tox-conda=0.8.3
-  - pytest=6.2.4
-  - pytest-codeblocks=0.11.2
-  # linting
-  - flake8=3.9.2
-  # docs
-  - sphinx=4.0.2
-  - sphinx_rtd_theme=0.5.1
-  - myst-parser==0.15.2
-  # # building
-  - build=0.6.0
-  # PIP
-  - pip=21.0.1
-  - pip:
-    - dicee==0.1.2
-    - owlready2==0.40
-    - owlapy==0.1.0
-    - tokenizers==0.12.1
-    - transformers==4.19.2
-    # testing
-    - tox-run-before==0.1
-    # docs
-    - sphinxext-autox==0.0.2
-    - sphinx-theme==1.0
-    - sphinxcontrib-plantuml==0.21
-    - plantuml-local-client==1.2021.10
-    - jinja2==3.1.2
diff --git a/ontolearn/nces_embeddings/util/experiment.py b/ontolearn/nces_embeddings/util/experiment.py
index af61ff64..ef17af6f 100644
--- a/ontolearn/nces_embeddings/util/experiment.py
+++ b/ontolearn/nces_embeddings/util/experiment.py
@@ -1,10 +1,10 @@
 import json
-
+import os
+import numpy as np
 import torch
-from .helper_funcs import *
-from .helper_classes import *
-from .complex_models import *
-from .real_models import *
+from ontolearn.nces_embeddings.util.complex_models import ConEx, Complex
+from ontolearn.nces_embeddings.util.helper_classes import HeadAndRelationBatchLoader, DatasetTriple
+from ontolearn.nces_embeddings.util.real_models import Distmult, Tucker, TransE
 from torch.optim.lr_scheduler import ExponentialLR
 from collections import defaultdict
 from torch.utils.data import DataLoader
diff --git a/ontolearn/nces_embeddings/util/helper_classes.py b/ontolearn/nces_embeddings/util/helper_classes.py
index 3987a43f..078098b0 100644
--- a/ontolearn/nces_embeddings/util/helper_classes.py
+++ b/ontolearn/nces_embeddings/util/helper_classes.py
@@ -1,8 +1,8 @@
 import json
-from .data import Data
-from .helper_funcs import *
-from .complex_models import Complex, ConEx
-from .real_models import Distmult, Tucker
+import json
+from ontolearn.nces_embeddings.util.data import Data
+from ontolearn.nces_embeddings.util.complex_models import Complex, ConEx
+from ontolearn.nces_embeddings.util.real_models import Distmult, Tucker
 from torch.optim.lr_scheduler import ExponentialLR
 from collections import defaultdict
 from torch.utils.data import DataLoader
diff --git a/requirements.txt b/requirements.txt
index 33365eeb..9203c295 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,18 +1,24 @@
-scikit-learn>=0.24.1
+scikit-learn>=1.0.2
 matplotlib>=3.3.4
-owlready2>=0.40
 torch>=1.7.1
 rdflib>=6.0.2
 pandas>=1.5.0
 sortedcontainers>=2.4.0
 flask>=1.1.2
 deap>=1.3.1
-httpx>=0.21.1
-parsimonious>=0.8.1
+httpx>=0.25.2
 tqdm>=4.64.0
-tokenizers>=0.12.1
-transformers>=4.19.2
-requests>=2.31.0
+transformers>=4.35.0
+owlready2>=0.41
 owlapy>=0.1.0
 dicee>=0.1.2
-pytest>=7.2.2
\ No newline at end of file
+flake8>=6.0.0
+sphinx>=7.2.6
+sphinx-autoapi>=3.0.0
+sphinx_rtd_theme>=2.0.0
+sphinx-theme>=1.0
+sphinxcontrib-plantuml>=0.27
+plantuml-local-client>=1.2022.6
+myst-parser>=2.0.0
+pytest>=7.2.2
+build>=1.0.3
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 01f5a4e7..00000000
--- a/setup.cfg
+++ /dev/null
@@ -1,65 +0,0 @@
-[metadata]
-name = ontolearn
-version = attr: ontolearn.__version__
-description = Ontolearn is an open-source software library for structured machine learning in Python. Ontolearn includes modules for processing knowledge bases, inductive logic programming and ontology engineering.
-author = Ontolearn team
-author_email = onto-learn@lists.uni-paderborn.de
-long_description = file: README.md
-long_description_content_type = text/markdown
-classifiers =
-    Programming Language :: Python :: 3.8
-    License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
-    Topic :: Scientific/Engineering :: Artificial Intelligence
-
-[options]
-python_requires = >=3.8
-include_package_data = True
-packages = find:
-scripts =
-    ontolearn/endpoint/simple_drill_endpoint
-install_requires =
-    typing_extensions; python_version < "3.7"
-    scikit-learn>=0.24.1
-    matplotlib>=3.3.4
-    owlready2>=0.40
-    torch>=1.7.1
-    rdflib>=6.0.2
-    pandas>=1.5.0
-    sortedcontainers>=2.4.0
-    flask>=1.1.2
-    deap>=1.3.1
-    httpx>=0.21.1
-    parsimonious>=0.8.1
-    tqdm>=4.64.0
-    tokenizers>=0.12.1
-    transformers>=4.19.2
-    dicee>=0.1.2
-
-[options.extras_require]
-test =
-    pytest>=6.2.4
-    pytest-codeblocks>=0.11.2
-    tox>=3.24.3
-    tox-run-before>=0.1
-docs =
-    sphinx>=4.0.2
-    sphinxext-autox>=0.0.2
-    sphinx-theme>=1.0
-    sphinx-rtd-theme>=0.5.1
-    sphinxcontrib-plantuml>=0.21
-    plantuml-local-client>=1.2021.10
-    myst-parser>=0.15.2
-lint =
-    flake8>=3.9.2
-dev =
-    build>=0.6.0
-    %(test)s
-    %(docs)s
-
-[options.packages.find]
-exclude =
-    tests
-    examples
-
-[options.package_data]
-* = *.pyi, py.typed
diff --git a/setup.py b/setup.py
index f74d8b13..135dc517 100644
--- a/setup.py
+++ b/setup.py
@@ -17,11 +17,9 @@
         "sortedcontainers>=2.4.0",
         "flask>=1.1.2",
         "deap>=1.3.1",
-        "httpx>=0.21.1",
-        "parsimonious>=0.8.1",
+        "httpx>=0.25.2",
         "tqdm>=4.64.0",
-        "tokenizers>=0.12.1",
-        "transformers>=4.19.2",
+        "transformers>=4.35.0",
         "pytest>=7.2.2",
         "owlapy>=0.1.0",
         "dicee>=0.1.2"],
diff --git a/tox.ini b/tox.ini
deleted file mode 100644
index 147f6c08..00000000
--- a/tox.ini
+++ /dev/null
@@ -1,55 +0,0 @@
-[flake8]
-max-line-length = 120
-
-[tox]
-isolated_build = True
-# investigate: higher versions of tox-conda break the build
-requires = tox-conda==0.9.2
-           tox-run-before
-envlist =
-    # py37
-    py38
-
-[testenv]
-conda_env = environment.yml
-conda_install_args=
-    --override-channels
-extras = test
-description = test Ontolearn
-run_before =
-    ./big_gitext/download_big.sh KGs/Mutagenesis/mutagenesis.owl
-commands =
-    pytest --continue-on-collection-errors --codeblocks {posargs} --ignore=./docs/usage
-
-[testenv:build]
-extras = dev
-description = build Ontolearn package
-commands =
-    python -m build
-
-[testenv:clean]
-skip_install = True
-description = delete the built package
-commands =
-    python -c 'from shutil import rmtree; rmtree("build", True); rmtree("dist", True)'
-
-[testenv:docs-clean]
-skip_install = True
-description = delete the built docs
-commands =
-    python -c 'from shutil import rmtree; rmtree("docs/_build", True);'
-
-[testenv:docs]
-extras = docs
-description = build Ontolearn docs
-commands =
-    sphinx-build -M {posargs:html} docs/ docs/_build/
-commands_post =
-    python -c 'print("documentation can be found in file://{toxinidir}/docs/_build/")'
-
-[testenv:lint]
-extras = lint
-skip_install = True
-description = lint the source code with flake8
-commands =
-    flake8 . {posargs}