cosmetic changes to docs

README.md

@@ -9,19 +9,13 @@ tags:
 - geography
 pretty_name: Geography
 ---
-<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;">Geography 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 geography domain encompasses the structured representation and analysis of spatial, environmental, and geopolitical phenomena, focusing on the precise modeling of physical locations, territorial boundaries, and place names. This domain is pivotal in knowledge representation as it facilitates the integration and interoperability of geographic information across diverse systems, enabling advanced spatial reasoning and decision-making. By providing a formal framework for understanding the complex relationships between geographic entities, this domain supports a wide range of applications, from urban planning to environmental monitoring.
@@ -42,18 +36,42 @@ Each ontology directory contains the following files:
 4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations
 5. `<ontology_id>.rst` - Documentation describing the ontology
 
+## Dataset Files
+Each ontology directory contains the following files:
+1. `<ontology_id>.<format>` - The original ontology file
+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:
+
+```bash
+pip install ontolearner
+```
+
+**How to load an ontology or LLM4OL Paradigm tasks datasets?**
+``` python
+from ontolearner import GEO
+
+ontology = GEO()
 
-ontology = Wine()
-ontology.load()  # Automatically downloads from Hugging Face
+# Load an ontology.
+ontology.load()  
 
-# Extract the dataset
+# Load (or extract) LLMs4OL Paradigm tasks datasets
+data = ontology.extract()
+```
+
+**How use the loaded dataset for LLM4OL Paradigm task settings?**
+``` python
+from ontolearner import GEO, LearnerPipeline, train_test_split
+
+ontology = GEO()
+ontology.load() 
 data = ontology.extract()
 
 # Split into train and test sets
@@ -61,10 +79,10 @@ 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
@@ -76,5 +94,23 @@ 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}
+}
+```
+