Information-Needs-Guided Virtual Knowledge Graph Enrichment via Large Language Models

Abstract

Virtual Knowledge Graphs (VKGs) provide an effective solution for data integration by mapping heterogeneous data sources to a unified ontology. However, existing VKG construction frameworks primarily focus on one-shot construction, which often results in partial data coverage and support for only initial information needs. After the VKG has been deployed, new information needs will inevitably arise over time. Therefore, enriching VKGs to support evolving information needs remains an expert-intensive iterative task. In this work, we formulate the task of Information-Needs-Guided VKG Enrichment (IN-VKGE), and propose an iterative framework that leverages large language models to assess whether information needs can be supported using SPARQL execution feedback and generate ontology and mapping enrichment proposals. Experiments on two real-world VKGs show that our approach outperforms existing paradigms, and produces enrichment proposals that receive high expert ratings for effectively resolving the identified information needs.