cosmetic changes to docs

README.md

@@ -9,19 +9,13 @@ tags:
 - education
 pretty_name: Education
 ---
-<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;">Education Domain Ontologies</h1>
+   <a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a>
 </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)
-
-</div>
 
 ## Overview
 The education domain encompasses ontologies that systematically represent and organize knowledge related to learning content, educational programs, competencies, and teaching resources. This domain plays a critical role in facilitating semantic interoperability and enhancing the precision of information retrieval and management within educational contexts. By providing a structured framework for the representation of educational concepts and relationships, it supports the development of intelligent systems that can effectively process and utilize educational data.
@@ -36,23 +30,40 @@ The education domain encompasses ontologies that systematically represent and or
 ## 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
 
+
 ## 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 BIBFRAME
+
+ontology = BIBFRAME()
 
-# 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 BIBFRAME, LearnerPipeline, train_test_split
+
+ontology = BIBFRAME()
+ontology.load() 
 data = ontology.extract()
 
 # Split into train and test sets
@@ -60,10 +71,10 @@ 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
@@ -75,5 +86,21 @@ 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}
+}
+```