minor update to readme

README.md

@@ -18,7 +18,7 @@ pretty_name: Industry
 </div>
 
 ## Overview
-The "industry" domain encompasses ontologies that systematically represent and model the complex structures, processes, and interactions within industrial settings, including manufacturing systems, smart buildings, and equipment. This domain is pivotal in advancing knowledge representation by enabling the integration, interoperability, and automation of industrial processes, thereby facilitating improved efficiency, innovation, and decision-making. Through precise semantic frameworks, it supports the digital transformation and intelligent management of industrial operations.
+The industry domain encompasses ontologies that systematically represent and model the complex structures, processes, and interactions within industrial settings, including manufacturing systems, smart buildings, and equipment. This domain is pivotal in advancing knowledge representation by enabling the integration, interoperability, and automation of industrial processes, thereby facilitating improved efficiency, innovation, and decision-making. Through precise semantic frameworks, it supports the digital transformation and intelligent management of industrial operations.
 
 | Ontology ID | Full Name | Classes | Properties | Individuals |
 |-------------|-----------|---------|------------|-------------|
@@ -41,50 +41,80 @@ 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 import LearnerPipeline, AutoLearnerLLM, Wine, train_test_split
+First of all, install the `OntoLearner` library via PiP:
 
-# Load ontology (automatically downloads from Hugging Face)
-ontology = Wine()
-ontology.load()
+```bash
+pip install ontolearner
+```
+
+**How to load an ontology or LLM4OL Paradigm tasks datasets?**
+``` python
+from ontolearner import AUTO
+
+ontology = AUTO()
+
+# 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
+# Import core modules from the OntoLearner library
+from ontolearner import AUTO, LearnerPipeline, train_test_split
 
-# Extract the dataset
+# Load the AUTO ontology, which contains concepts related to wines, their properties, and categories
+ontology = AUTO()
+ontology.load()  # Load entities, types, and structured term annotations from the ontology
 data = ontology.extract()
 
 # Split into train and test sets
-train_data, test_data = train_test_split(data, test_size=0.2)
+train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
 
-# Create a learning pipeline (for RAG-based learning)
+# Initialize a multi-component learning pipeline (retriever + LLM)
+# This configuration enables a Retrieval-Augmented Generation (RAG) setup
 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
+    retriever_id='sentence-transformers/all-MiniLM-L6-v2',      # Dense retriever model for nearest neighbor search
+    llm_id='Qwen/Qwen2.5-0.5B-Instruct',                        # Lightweight instruction-tuned LLM for reasoning
+    hf_token='...',                                             # Hugging Face token for accessing gated models
+    batch_size=32,                                              # Batch size for training/prediction if supported
+    top_k=5                                                     # Number of top retrievals to include in RAG prompting
 )
 
-# Train and evaluate
-results, metrics = pipeline.fit_predict_evaluate(
+# Run the pipeline: training, prediction, and evaluation in one call
+outputs = pipeline(
     train_data=train_data,
     test_data=test_data,
-    top_k=3,
-    test_limit=10
+    evaluate=True,              # Compute metrics like precision, recall, and F1
+    task='term-typing'          # Specifies the task
+                                # Other options: "taxonomy-discovery" or "non-taxonomy-discovery"
 )
+
+# Print final evaluation metrics
+print("Metrics:", outputs['metrics'])
+
+# Print the total time taken for the full pipeline execution
+print("Elapsed time:", outputs['elapsed_time'])
+
+# Print all outputs (including predictions)
+print(outputs)
 ```
 
 For more detailed examples, see the [OntoLearner documentation](https://ontolearner.readthedocs.io/).
 
 ## Citation
-If you use these ontologies in your research, please cite:
+
+If you find our work helpful, feel free to give us a cite.
 
 ```bibtex
-@software{babaei_giglou_2025,
-  author       = {Babaei Giglou, Hamed and D'Souza, Jennifer and Aioanei, Andrei and Mihindukulasooriya, Nandana and Auer, Sören},
-  title        = {OntoLearner: A Modular Python Library for Ontology Learning with LLMs},
-  month        = may,
-  year         = 2025,
-  publisher    = {Zenodo},
-  version      = {v1.0.1},
-  doi          = {10.5281/zenodo.15399783},
-  url          = {https://doi.org/10.5281/zenodo.15399783},
+@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}
 }
 ```