cosmetic changes to docs

README.md

@@ -9,18 +9,11 @@ tags:
 - general_knowledge
 pretty_name: General Knowledge
 ---
-<div>
+<div align="center">
   <img  src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png"  alt="OntoLearner"
     style="display: block; margin: 0 auto; width: 500px; height: auto;">
   <h1 style="text-align: center; margin-top: 1em;">General Knowledge Domain Ontologies</h1>
-</div>
-
-<div align="center">
-  
-[![GitHub](https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github)](https://github.com/sciknoworg/OntoLearner)
-[![PyPI](https://img.shields.io/badge/PyPI-OntoLearner-blue?logo=pypi)](https://pypi.org/project/OntoLearner/)
-[![Documentation](https://img.shields.io/badge/Docs-ReadTheDocs-blue)](https://ontolearner.readthedocs.io/benchmarking/benchmark.html)
-
+  <a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a>
 </div>
 
 ## Overview
@@ -44,7 +37,7 @@ The general_knowledge domain encompasses broad-scope ontologies and upper vocabu
 ## Dataset Files
 Each ontology directory contains the following files:
 1. `<ontology_id>.<format>` - The original ontology file
-2. `term_typings.json` - Dataset of term to type mappings
+2. `term_typings.json` - A Dataset of term-to-type mappings
 3. `taxonomies.json` - Dataset of taxonomic relations
 4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations
 5. `<ontology_id>.rst` - Documentation describing the ontology
@@ -52,15 +45,31 @@ Each ontology directory contains the following files:
 ## Usage
 These datasets are intended for ontology learning research and applications. Here's how to use them with OntoLearner:
 
-```python
-from ontolearner.ontology import Wine
-from ontolearner.utils.train_test_split import train_test_split
-from ontolearner.learner_pipeline import LearnerPipeline
+First of all, install the `OntoLearner` library via PiP:
 
-ontology = Wine()
-ontology.load()  # Automatically downloads from Hugging Face
+```bash
+pip install ontolearner
+```
+
+**How to load an ontology or LLM4OL Paradigm tasks datasets?**
+``` python
+from ontolearner import CCO
+
+ontology = CCO()
 
-# Extract the dataset
+# Load an ontology.
+ontology.load()  
+
+# Load (or extract) LLMs4OL Paradigm tasks datasets
+data = ontology.extract()
+```
+
+**How use the loaded dataset for LLM4OL Paradigm task settings?**
+``` python
+from ontolearner import CCO, LearnerPipeline, train_test_split
+
+ontology = CCO()
+ontology.load() 
 data = ontology.extract()
 
 # Split into train and test sets
@@ -68,10 +77,10 @@ train_data, test_data = train_test_split(data, test_size=0.2)
 
 # Create a learning pipeline (for RAG-based learning)
 pipeline = LearnerPipeline(
-    task="term-typing",  # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
-    retriever_id="sentence-transformers/all-MiniLM-L6-v2", 
-    llm_id="mistralai/Mistral-7B-Instruct-v0.1",
-    hf_token="your_huggingface_token"  # Only needed for gated models
+    task = "term-typing",  # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
+    retriever_id = "sentence-transformers/all-MiniLM-L6-v2", 
+    llm_id = "mistralai/Mistral-7B-Instruct-v0.1",
+    hf_token = "your_huggingface_token"  # Only needed for gated models
 )
 
 # Train and evaluate
@@ -83,5 +92,21 @@ results, metrics = pipeline.fit_predict_evaluate(
 )
 ```
 
-For more detailed examples, see the [OntoLearner documentation](https://ontolearner.readthedocs.io/).
-    
+For more detailed documentation, see the [![Documentation](https://img.shields.io/badge/Documentation-ontolearner.readthedocs.io-blue)](https://ontolearner.readthedocs.io)
+
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+
+```bibtex
+@inproceedings{babaei2023llms4ol,
+  title={LLMs4OL: Large language models for ontology learning},
+  author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{\"o}ren},
+  booktitle={International Semantic Web Conference},
+  pages={408--427},
+  year={2023},
+  organization={Springer}
+}
+```