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Virtual Knowledge Graphs is used—a paradigm that presents data to end-users as a knowledge graph, while being kept its original sources and formats to bridge the semantic gap between EO data and these other sources.
This work forms the task of Information-Needs-Guided VKG Enrichment (IN-VKGE), and proposes 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.
This paper studies the VKG-QA task, which enables users to interact with the VKGs through a natural language (NL) interface by translating their questions into SPARQL queries, and pro-poses NaVQA (Navigation-based VKG Question Answering), a framework leveraging Large Language Models.
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