LHRLAB/ChatKBQA
[ACL 2024] Official resources of "ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models".
This project helps anyone who needs to quickly find precise answers to natural language questions from a vast knowledge base, like Wikipedia or Freebase. You input a question in plain English, and it outputs a precise, structured query (like SPARQL) that can be executed against a knowledge graph, along with the correct answer. It's designed for data analysts, researchers, or anyone working with large, interconnected datasets who needs accurate information without manually constructing complex database queries.
337 stars. No commits in the last 6 months.
Use this if you need to reliably convert everyday questions into executable queries for large knowledge graphs, especially when high accuracy and structured outputs are critical.
Not ideal if your data isn't structured as a knowledge graph or if you primarily work with unstructured text documents rather than factual entities and their relationships.
Stars
337
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30
Language
Python
License
MIT
Category
Last pushed
Sep 22, 2025
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