minor update to readme

README.md

@@ -6,7 +6,7 @@ language:
 tags:
 - OntoLearner
 - ontology-learning
-- general_knowledge
+- general-knowledge
 pretty_name: General Knowledge
 ---
 <div align="center">
@@ -17,7 +17,7 @@ pretty_name: General Knowledge
 </div>
 
 ## Overview
-The general_knowledge domain encompasses broad-scope ontologies and upper vocabularies designed for cross-disciplinary semantic modeling and knowledge representation. This domain is pivotal in facilitating interoperability and data integration across diverse fields by providing a foundational framework for organizing and linking information. Its significance lies in enabling the seamless exchange and understanding of knowledge across varied contexts, thereby supporting advanced data analysis, information retrieval, and decision-making processes.
+The general knowledge domain encompasses broad-scope ontologies and upper vocabularies designed for cross-disciplinary semantic modeling and knowledge representation. This domain is pivotal in facilitating interoperability and data integration across diverse fields by providing a foundational framework for organizing and linking information. Its significance lies in enabling the seamless exchange and understanding of knowledge across varied contexts, thereby supporting advanced data analysis, information retrieval, and decision-making processes.
 
 ## Ontologies
 | Ontology ID | Full Name | Classes | Properties | Last Updated |
@@ -66,30 +66,44 @@ data = ontology.extract()
 
 **How use the loaded dataset for LLM4OL Paradigm task settings?**
 ``` python
+# Import core modules from the OntoLearner library
 from ontolearner import CCO, LearnerPipeline, train_test_split
 
+# Load the CCO ontology, which contains concepts related to wines, their properties, and categories
 ontology = CCO()
-ontology.load() 
+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 documentation, see the [![Documentation](https://img.shields.io/badge/Documentation-ontolearner.readthedocs.io-blue)](https://ontolearner.readthedocs.io)