zjukg/KoPA
[Paper][ACM MM 2024] Making Large Language Models Perform Better in Knowledge Graph Completion
This project helps improve how large language models (LLMs) fill in missing information within knowledge graphs. It takes an existing knowledge graph with gaps and an LLM, then enriches the graph by predicting missing connections, making the LLM more accurate in its reasoning. This is for developers and researchers who build or manage web-based services that rely on comprehensive knowledge graphs.
214 stars. No commits in the last 6 months.
Use this if you need to enhance the accuracy of large language models when they are used to complete or expand knowledge graphs for web services.
Not ideal if you are looking for an out-of-the-box, end-user application for knowledge graph completion, as this is a developer tool.
Stars
214
Forks
16
Language
Python
License
MIT
Category
Last pushed
Aug 12, 2024
Commits (30d)
0
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