AutoVU-KG: Automated Validation and Updates for Knowledge Graphs with Web-Search-Augmented LLMs

Amel Gader, Alsayed Algergawy

2025

Abstract

Knowledge Graphs (KGs) offer a powerful framework for representing and managing structured information in many applications. However, when it comes to frequently changing facts, KGs often lag behind real-world updates. Large Language Models (LLMs) hold promise for enriching and updating KGs, but their capabilities are limited by static training cutoffs and a tendency to hallucinate or produce outdated information. To address these concerns, we introduce AutoVUKG: Automated Validation and Updates for Knowledge Graphs with Web-Search-Augmented LLMs. Our approach comprises: a classification module that identifies facts likely to change and therefore needing updates; An LLM-driven validation and update pipeline, enhanced with real-time web retrieval to ground assertions in current external sources, and an entity matching and alignment component that ensures updates maintain internal consistency within the KG. Evaluation on subsets of Wikidata demonstrates that the proposed approach achieves high accuracy and significantly outperforms vanilla LLMs. Additionally, it reduces the number of outdated facts by up to 60% on one of the datasets. The source code is available at https://github.com/amal-gader/autovu-kg.

Paper Citation

in Harvard Style

Gader A. and Algergawy A. (2025). AutoVU-KG: Automated Validation and Updates for Knowledge Graphs with Web-Search-Augmented LLMs. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 257-265. DOI: 10.5220/0013704600004000

in Bibtex Style

@conference{kdir25,
author={Amel Gader and Alsayed Algergawy},
title={AutoVU-KG: Automated Validation and Updates for Knowledge Graphs with Web-Search-Augmented LLMs},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={257-265},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013704600004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - AutoVU-KG: Automated Validation and Updates for Knowledge Graphs with Web-Search-Augmented LLMs
SN -
AU - Gader A.
AU - Algergawy A.
PY - 2025
SP - 257
EP - 265
DO - 10.5220/0013704600004000
PB - SciTePress