AI / NLP
Knowledge Graph Q&A System
An end-to-end pipeline that scrapes web data, extracts entities, builds a knowledge graph in Neo4j, and enables natural language question answering.
Problem
Unstructured web data is difficult to query and reason about. Traditional search fails to capture relationships between entities.
Solution
Built an automated pipeline: web scraping → entity extraction (NER) → relationship mapping → Neo4j storage → graph-based Q&A system.
Architecture
Web Scraping
Automated data collection from web sources
Entity Extraction
NER-based entity identification
Relationship Mapping
Entity relationship identification
Neo4j Storage
Graph database for structured storage
Q&A Interface
Natural language query interface
Technologies
Lessons Learned
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Graph databases excel at relationship-heavy queries
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NER accuracy directly impacts downstream graph quality
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Web scraping requires robust error handling and rate limiting