KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction

TL;DR

KGValidator leverages Large Language Models to automate the validation of knowledge graphs, reducing reliance on costly human annotation by using generative AI for structural, semantic, and factual verification.

Contribution

Introduces a flexible framework utilizing LLMs for automatic validation of knowledge graphs, integrating external knowledge sources and adaptable validation strategies.

Findings

Framework effectively automates KG validation tasks.

Supports referencing external knowledge for improved accuracy.

Easy to adapt for different types of graph-structured data.

Abstract

This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been a challenging task, requiring large-scale human annotation at prohibitive cost. With the emergence of general-purpose generative AI and LLMs, it is now plausible that human-in-the-loop validation could be replaced by a generative agent. We introduce a framework for consistency and validation when using generative models to validate knowledge graphs. Our framework is based upon recent open-source developments for structural and semantic validation of LLM outputs, and upon flexible approaches to fact checking and verification, supported by the capacity to reference external knowledge sources of any kind. The design is easy to adapt and extend, and can be used to verify any kind of graph-structured data through a combination of model-intrinsic knowledge, user-supplied context, and agents capable of external knowledge retrieval.