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        <title>Can LLMs be Knowledge Graph Curators for Validating Triple Insertions?</title>
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    <abstract>As Knowledge Graphs (KGs) become central to modern applications, automated methods for validating RDF triples before insertion into these graphs are essential. The complexity and scalability challenges in manual validation processes have led researchers to explore Large Language Models (LLMs) as potential automated validators. This study investigates the feasibility of using LLMs to validate RDF triples by focusing on four distinct and complementary validation tasks: class and property alignment, URI standardization, semantic consistency, and syntactic correctness. We propose a systematic validation method that uses prompts to guide LLMs through each stage of the triple evaluation of the RDF. In our experiments, four models are evaluated across these tasks. Our results reveal that more advanced models like Llama-3-70B-Instruct offer superior accuracy and consistency. Our findings emphasize the practical open challenges of deploying LLMs in real-world RDF validation scenarios, including domain generalization, semantic drift, and the need for human-in-the-loop interventions. This investigation advances the research on the refinement and integration of LLM-based RDF validation techniques into KG management workflows.</abstract>
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%0 Conference Proceedings
%T Can LLMs be Knowledge Graph Curators for Validating Triple Insertions?
%A Regino, André Gomes
%A dos Reis, Julio Cesar
%Y Gesese, Genet Asefa
%Y Sack, Harald
%Y Paulheim, Heiko
%Y Merono-Penuela, Albert
%Y Chen, Lihu
%S Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F regino-dos-reis-2025-llms
%X As Knowledge Graphs (KGs) become central to modern applications, automated methods for validating RDF triples before insertion into these graphs are essential. The complexity and scalability challenges in manual validation processes have led researchers to explore Large Language Models (LLMs) as potential automated validators. This study investigates the feasibility of using LLMs to validate RDF triples by focusing on four distinct and complementary validation tasks: class and property alignment, URI standardization, semantic consistency, and syntactic correctness. We propose a systematic validation method that uses prompts to guide LLMs through each stage of the triple evaluation of the RDF. In our experiments, four models are evaluated across these tasks. Our results reveal that more advanced models like Llama-3-70B-Instruct offer superior accuracy and consistency. Our findings emphasize the practical open challenges of deploying LLMs in real-world RDF validation scenarios, including domain generalization, semantic drift, and the need for human-in-the-loop interventions. This investigation advances the research on the refinement and integration of LLM-based RDF validation techniques into KG management workflows.
%U https://aclanthology.org/2025.genaik-1.10/
%P 87-99

Markdown (Informal)

[Can LLMs be Knowledge Graph Curators for Validating Triple Insertions?](https://aclanthology.org/2025.genaik-1.10/) (Regino & dos Reis, GenAIK 2025)

• Can LLMs be Knowledge Graph Curators for Validating Triple Insertions? (Regino & dos Reis, GenAIK 2025)

ACL

• André Gomes Regino and Julio Cesar dos Reis. 2025. Can LLMs be Knowledge Graph Curators for Validating Triple Insertions?. In Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK), pages 87–99, Abu Dhabi, UAE. International Committee on Computational Linguistics.