A Multi-Agent System for Semantic Mapping of Relational Data to Knowledge Graphs
This addresses data interoperability challenges for enterprises, though it appears incremental as it builds on existing vocabularies like Schema.org.
The paper tackles the problem of integrating siloed enterprise databases by developing a multi-agent system that uses large language models to map relational data to knowledge graphs, achieving over 90% mapping accuracy across domains.
Enterprises often maintain multiple databases for storing critical business data in siloed systems, resulting in inefficiencies and challenges with data interoperability. A key to overcoming these challenges lies in integrating disparate data sources, enabling businesses to unlock the full potential of their data. Our work presents a novel approach for integrating multiple databases using knowledge graphs, focusing on the application of large language models as semantic agents for mapping and connecting structured data across systems by leveraging existing vocabularies. The proposed methodology introduces a semantic layer above tables in relational databases, utilizing a system comprising multiple LLM agents that map tables and columns to Schema.org terms. Our approach achieves a mapping accuracy of over 90% in multiple domains.