cosmetic changes to docs

README.md

@@ -9,12 +9,14 @@ tags:
 - scholarly_knowledge
 pretty_name: Agricultural
 ---
-<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;">Scholarly Knowledge Domain Ontologies</h1>
+  <a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a>
 </div>
 
+
 ## Overview
 The scholarly_knowledge domain encompasses ontologies that systematically model the intricate structures, processes, and administrative mechanisms underlying scholarly research, publications, and associated infrastructures. This domain plays a critical role in the formal representation and organization of academic knowledge, facilitating interoperability, data sharing, and enhanced understanding across diverse research disciplines. By providing a structured framework for capturing the complexities of scholarly activities, these ontologies support the advancement of research methodologies and the dissemination of scientific knowledge.
 
@@ -49,11 +51,76 @@ The scholarly_knowledge domain encompasses ontologies that systematically model
 ## 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.
-    
+These datasets are intended for ontology learning research and applications. Here's how to use them with OntoLearner:
+
+First of all, install the `OntoLearner` library via PiP:
+
+```bash
+pip install ontolearner
+```
+
+**How to load an ontology or LLM4OL Paradigm tasks datasets?**
+``` python
+from ontolearner import AIISO
+
+ontology = AIISO()
+
+# 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 AIISO, LearnerPipeline, train_test_split
+
+ontology = AIISO()
+ontology.load() 
+data = ontology.extract()
+
+# Split into train and test sets
+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
+)
+
+# Train and evaluate
+results, metrics = pipeline.fit_predict_evaluate(
+    train_data=train_data,
+    test_data=test_data,
+    top_k=3,
+    test_limit=10
+)
+```
+
+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}
+}
+```