Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration but typically require significant expertise for their construction. This process, involving ontology development, schema analysis, and mapping creation, is often hindered by naming ambiguities and matching issues, which traditional rule-based methods struggle to address. Large language models (LLMs), with their ability to process and generate contextually relevant text, offer a potential solution. In this work, we introduce LLM4VKG, a novel framework that leverages LLMs to automatize VKG construction. Experimental evaluation on the RODI benchmark demonstrates that LLM4VKG surpasses state-of-the-art methods, achieving an average F1-score improvement of +17% and a peak gain of +39%. Moreover, LLM4VKG proves robust against incomplete ontologies and can handle complex mappings where current methods fail.
LLM4VKG: Leveraging Large Language Models for Virtual Knowledge Graph Construction / Xiao, Guohui; Ren, Lin; Qi, Guilin; Xue, Haohan; Di Panfilo, Marco; Lanti, Davide. - (2025). (Intervento presentato al convegno Thirty-Fourth International Joint Conference on Artificial Intelligence tenutosi a Montreal) [10.24963/ijcai.2025/525].
LLM4VKG: Leveraging Large Language Models for Virtual Knowledge Graph Construction
Marco Di Panfilo;
2025
Abstract
Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration but typically require significant expertise for their construction. This process, involving ontology development, schema analysis, and mapping creation, is often hindered by naming ambiguities and matching issues, which traditional rule-based methods struggle to address. Large language models (LLMs), with their ability to process and generate contextually relevant text, offer a potential solution. In this work, we introduce LLM4VKG, a novel framework that leverages LLMs to automatize VKG construction. Experimental evaluation on the RODI benchmark demonstrates that LLM4VKG surpasses state-of-the-art methods, achieving an average F1-score improvement of +17% and a peak gain of +39%. Moreover, LLM4VKG proves robust against incomplete ontologies and can handle complex mappings where current methods fail.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
