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This project builds a Financial Knowledge Graph from SET50 data, comparing its query efficiency and accuracy with a relational database to enhance complex financial analysis.

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naphattha/A-Financial-Knowledge-Graph-Construction-from-SET50-Annual-Reports

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A Financial Knowledge Graph Construction from SET50 Annual Reports

This project provides a chatbot interface to query financial data for SET50 companies. It compares performance between two databases, Neo4j (graph-based) and MySQL (relational), in handling complex financial queries. The project leverages a Large Language Model (LLM) for natural language interpretation.

Project Structure

  • llm-chatbot-python-neo4j: Contains code for Neo4j database integration.
  • llm-chatbot-python-mysql: Contains code for MySQL database integration.

Each directory includes the necessary files to run an independent instance of the chatbot application.

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This project builds a Financial Knowledge Graph from SET50 data, comparing its query efficiency and accuracy with a relational database to enhance complex financial analysis.

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