Knowledge Graph Construction – AI-based Information Extraction

This project uses Artificial Intelligence (AI) to transform unstructured text into structured knowledge. By applying Natural Language Processing (NLP), the system extracts entities, finds relationships, and builds a knowledge graph that connects concepts : making information easier to explore and understand.

How AI Was Used

Several AI models were combined to extract and connect knowledge automatically:

• spaCy – detects named entities such as people, places, and organizations.
• Relation Extraction – links entities based on syntactic and semantic relations.
• RDFLib – converts information into an RDF graph for structured representation.
• PyKEEN – learns embeddings to uncover hidden connections and predict relationships.

Results

The AI successfully generated a knowledge graph from real-world text data, linking entities through semantic relationships. It allows interactive exploration of how people, places, and organizations are connected : demonstrating how AI helps organize complex information efficiently.

Conclusion: AI transforms plain text into structured knowledge, enabling deeper insights and smarter search. This project highlights how NLP and graph learning can make unstructured data more meaningful and connected.