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Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching

A proposed model for schema matching using knowledge graphs and retrieval-augmented generation.

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Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching

By Chuangtao Ma, Chakrabarti, Sriom, Arijit Khan, Bálint MolnárarXiv (Cornell University)
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The authors propose a Knowledge Graph-based Retrieval-Augmented Generation model (KG-RAG4SM) to address semantic ambiguities in schema matching. The model introduces novel vector-based, graph traversal-based, and query-based graph retrievals.

Experimental results show that KG-RAG4SM outperforms state-of-the-art methods in terms of precision and F1 score on various datasets.

Abstract

The authors propose a Knowledge Graph-based Retrieval-Augmented Generation model (KG-RAG4SM) to address semantic ambiguities in schema matching. The model introduces novel vector-based, graph traversal-based, and query-based graph retrievals. Experimental results show that KG-RAG4SM outperforms state-of-the-art methods in terms of precision and F1 score on various datasets.

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schema matchingknowledge graph retrievalaugmented generationsemantic interoperabilitylarge language modelsKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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