Summary of Kgvalidator: a Framework For Automatic Validation Of Knowledge Graph Construction, by Jack Boylan et al.
KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction
by Jack Boylan, Shashank Mangla, Dominic Thorn, Demian Gholipour Ghalandari, Parsa Ghaffari, Chris Hokamp
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A study utilizes Large Language Models (LLMs) for evaluating knowledge graph (KG) completion models. Historically, validating information in KGs has been challenging due to the need for large-scale human annotation at a prohibitive cost. This work introduces a framework for consistency and validation when using generative models to validate knowledge graphs. The framework is based on recent open-source developments for structural and semantic validation of LLM outputs, as well as flexible approaches to fact checking and verification supported by external knowledge sources. This design can be easily adapted and extended to verify any graph-structured data through a combination of model-intrinsic knowledge, user-supplied context, and agents capable of external knowledge retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being explored to help computers understand how accurate they are when completing information in large networks of related facts. This is important because right now it’s very hard for humans to check all the data by hand. The researchers developed a system that uses special AI models and combines them with human input and external knowledge sources to make sure the data is correct. This approach can be used to verify many different types of networked data. |
Keywords
» Artificial intelligence » Knowledge graph
