Refactoring (removed owlapy), verbalization, documentation update and some fixes

README.md

@@ -8,7 +8,7 @@ Learning algorithms:
 - **EvoLearner** → [EvoLearner: Learning Description Logics with Evolutionary Algorithms](https://dl.acm.org/doi/abs/10.1145/3485447.3511925)
 - **NCES2** → (soon) [Neural Class Expression Synthesis in ALCHIQ(D)](https://papers.dice-research.org/2023/ECML_NCES2/NCES2_public.pdf)
 - **NCES** → [Neural Class Expression Synthesis](https://link.springer.com/chapter/10.1007/978-3-031-33455-9_13) 
-- **NERO** → [Learning Permutation-Invariant Embeddings for Description Logic Concepts](https://link.springer.com/chapter/10.1007/978-3-031-30047-9_9)
+- **NERO** → (soon) [Learning Permutation-Invariant Embeddings for Description Logic Concepts](https://link.springer.com/chapter/10.1007/978-3-031-30047-9_9)
 - **CLIP** → (soon) [Learning Concept Lengths Accelerates Concept Learning in ALC](https://link.springer.com/chapter/10.1007/978-3-031-06981-9_14)
 - **CELOE** → [Class Expression Learning for Ontology Engineering](https://www.sciencedirect.com/science/article/abs/pii/S1570826811000023)
 - **OCEL** → A limited version of CELOE
@@ -34,9 +34,9 @@ from ontolearn.knowledge_base import KnowledgeBase
 from ontolearn.concept_learner import CELOE, OCEL, EvoLearner, Drill
 from ontolearn.learning_problem import PosNegLPStandard
 from ontolearn.metrics import F1
-from ontolearn.owlapy.model import OWLNamedIndividual, IRI
+from owlapy.model import OWLNamedIndividual, IRI
 from ontolearn.utils import setup_logging
-from ontolearn.owlapy.render import DLSyntaxObjectRenderer
+from owlapy.render import DLSyntaxObjectRenderer
 setup_logging()
 renderer = DLSyntaxObjectRenderer()
 max_runtime, topk=1, 3
@@ -63,6 +63,9 @@ Fore more please refer to  the [examples](https://github.com/dice-group/Ontolear
 
 ## Deployment 
 
+```shell
+pip install gradio
+```
 
 To deploy **EvoLearner** on the **Family** knowledge graph. Available models to deploy: **EvoLearner**, **NCES**, **CELOE** and **OCEL**.
 ```shell

deploy_cl.py

@@ -1,6 +1,5 @@
 import pandas as pd
 import torch
-import gradio as gr
 from argparse import ArgumentParser
 import random
 import os
@@ -15,8 +14,14 @@
 from ontolearn.learning_problem import PosNegLPStandard
 from ontolearn.refinement_operators import ModifiedCELOERefinement
 from ontolearn.value_splitter import EntropyValueSplitter, BinningValueSplitter
-from ontolearn.owlapy.model import OWLNamedIndividual, IRI
-from ontolearn.owlapy.render import DLSyntaxObjectRenderer
+from owlapy.model import OWLNamedIndividual, IRI
+from owlapy.render import DLSyntaxObjectRenderer
+
+try:
+    import gradio as gr
+except ImportError as e:
+    raise ImportError("Gradio not found! Please install gradio to use this script --> `pip install gradio`")
+
 
 metrics = {'F1': F1,
            'Accuracy': Accuracy,
@@ -141,15 +146,15 @@ def predict(positive_examples, negative_examples, random_examples, size_of_examp
                         with gr.Row():
                             i6 = gr.Checkbox(label="Terminate on goal", value=True)
                             i8 = gr.Checkbox(label="Use data properties", value=True)
-                            i9 = gr.Checkbox(label="Use card restrictions", value=True)
+                            i9 = gr.Checkbox(label="Use cardinality restrictions for object properties", value=True)
                             i10 = gr.Checkbox(label="Use inverse", value=False)
                         with gr.Row():
                             i7 = gr.Number(label="Maximum runtime", value=600)
                             i11 = gr.Number(label="Tournament size", value=7)
                             i13 = gr.Number(label="Population size", value=800)
                         with gr.Row():
                             i14 = gr.Number(label="Num generations", value=200)
-                            i12 = gr.Number(label="Card limit", value=10)
+                            i12 = gr.Number(label="Cardinality limit for object properties", value=10)
                             i15 = gr.Number(label="Height limit", value=17)
                     gr.Markdown("Set arguments for the fitness function (LinearPressureFitness)")
                     with gr.Box():
@@ -291,8 +296,8 @@ def predict(positive_examples, negative_examples, random_examples: bool, size_of
                                             info="For the value splitter: BinningValueSplitter")
                             i18 = gr.Number(label="Maximum child length", value=10, info="\n")
                         with gr.Row():
-                            i22 = gr.Checkbox(label="Use card restrictions", value=True)
-                            i26 = gr.Number(label="Card limit", value=10)
+                            i22 = gr.Checkbox(label="Use cardinality restrictions for object properties", value=True)
+                            i26 = gr.Number(label="Cardinality limit for object properties", value=10)
                         with gr.Row():
                             i19 = gr.Checkbox(label="Use negation", value=True)
                             i20 = gr.Checkbox(label="Use all constructors", value=True)
@@ -405,8 +410,8 @@ def predict(positive_examples, negative_examples, random_examples: bool, size_of
                                             info="For the value splitter: BinningValueSplitter")
                             i16 = gr.Number(label="Maximum child length", value=10, info="\n")
                         with gr.Row():
-                            i20 = gr.Checkbox(label="Use card restrictions", value=True)
-                            i24 = gr.Number(label="Card limit", value=10)
+                            i20 = gr.Checkbox(label="Use cardinality restrictions for object properties", value=True)
+                            i24 = gr.Number(label="Cardinality limit for object properties", value=10)
                         with gr.Row():
                             i17 = gr.Checkbox(label="Use negation", value=True)
                             i18 = gr.Checkbox(label="Use all constructors", value=True)

docs/usage/01_introduction.md

@@ -14,19 +14,24 @@
 
 Ontolearn is an open-source software library for explainable structured machine learning in Python.
 
-For the core module [owlapy](ontolearn.owlapy) Ontolearn is based on [Owlready2](https://owlready2.readthedocs.io/en/latest/index.html), 
-a package for manipulating OWL 2.0 ontologies in Python. In addition, we have implemented
-a higher degree of code for manipulation OWL 2.0 ontologies, in pursuit of making it 
-easier, more flexible and of course, having this all in Python. This adaptation of 
-Owlready2 library made it possible to build more complex algorithms.
-
 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 4 fully functioning algorithms that 
 learn concept in description logics. Papers can be found [here](09_further_resources.md).
 
-Ontolearn can do the following:
+For the base (core) module of Ontolearn we use [owlapy](https://github.com/dice-group/owlapy)
+combined with [Owlready2](https://owlready2.readthedocs.io/en/latest/index.html). _Owlapy_ is a python package
+based on owlapi and implemented by us, the Ontolearn team. For the sake of modularization we
+have moved it in a separate repository. On the other side _Owlready2_ is a package for manipulating 
+OWL 2.0 ontologies in Python. So in the end we have implemented
+a higher degree of code for manipulation OWL 2.0 ontologies, in pursuit of making it 
+easier to implement and understand, and of course, having this all in Python. This adaptation of 
+Owlready2 library made it possible to build more complex algorithms.
+
+---------------------------------------
+
+**Ontolearn (including owlapy) can do the following:**
 
 - Load/save ontologies in RDF/XML, OWL/XML.
 - Modify ontologies by adding/removing axioms.
@@ -38,6 +43,8 @@ Ontolearn can do the following:
 - Reason over an ontology.
 - Other convenient functionalities like converting OWL class expressions to SPARQL or DL syntax.
 
+------------------------------------
+
 The rest of the content is build as a top-to-bottom guide, but nevertheless self-containing, where
 you can learn more in depth about the capabilities of Ontolearn.
 

docs/usage/02_installation.md

@@ -12,6 +12,20 @@ One such system for virtual python environments is
 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.
+
+```shell
+pip install ontolearn
+```
+
+This will download and install the latest release version of Ontolearn
+and all its dependencies from <https://pypi.org/project/ontolearn/>.
+
 ## Installation From Source
 
 To download the Ontolearn source code, you will also need to have a
@@ -34,11 +48,11 @@ installation links to get started with the library.
   git clone https://github.com/dice-group/Ontolearn.git
   cd Ontolearn
   ```
-* Load the dependencies into a new conda environment called "temp" (you can name it however you like)
+
+* Create a conda environment using the `environment.yml` file.
   ```shell
-  conda create --name temp python=3.8
-  conda activate temp
-  conda env update --name temp
+  conda env create -f environment.yml
+  conda activate ontolearn
   ```
 * Install the development links so that Python will find the library
   ```shell
@@ -69,21 +83,6 @@ to successfully pass all the tests:
 pytest
 ```
 
-
-## Installation via _pip_
-
-Released versions of Ontolearn can also be installed using `pip`, the
-Package Installer for Python. It comes as part of Python. Please
-research externally (or use above `conda create` command) on how to
-create virtual environments for Python programs.
-
-```shell
-pip install ontolearn
-```
-
-This will download and install the latest release version of Ontolearn
-and all its dependencies from <https://pypi.org/project/ontolearn/>.
-
 ## Download External Files
 
 Some resources like pre-calculated embeddings or `pre_trained_agents` and datasets (ontologies)