Up To Date: Automatic Updating Knowledge Graphs Using LLMs
Shahenda Hatem, Ghada Ahmed Khoriba, Mohamed H. Gad-Elrab, Mohamed Elhelw · Procedia Computer Science · 2024
Maintaining up-to-date knowledge graphs (KGs) is essential for enhancing the accuracy and relevance of artificial intelligence (AI) applications, especially with sensitive domains. Yet, major KGs are either manually maintained (e.g., Wikidata) or infrequently rebuilt (e.g., DBpedia & YAGO). Thus, they contain many outdated facts. The rise of Large Language Models (LLMs) reasoning and Augmented Retrieval Generation approaches (RAG) gives KGs an interface to other trusted sources. This paper introduces a methodology utilizing Large Language Models (LLMs) to validate and update KG facts automatically. In particular, we utilize LLM reasoning capabilities to determine potentially outdated facts. After that, we use RAG techniques to generate an accurate fix for the fact. Experimental results on several LLMs and real-world datasets demonstrate the ability of our approach to propose accurate fixes. In addition, our experiments highlight the efficacy of few-shot prompts over zero-shot prompts.