用大模型将多个数据库的表格列映射到知识图谱，提升企业数据互通性。
A Multi-Agent System for Semantic Mapping of Relational Data to Knowledge Graphs
- 多智能体系统利用大模型将数据库表列映射到Schema.org术语
- 跨领域映射准确率超90%，有效连接异构数据源
- 适合需要整合分散业务数据的企业和数据工程师
企业常在孤立系统中维护多个数据库，导致数据难以互通。本文提出一种基于知识图谱的新方法，通过大语言模型作为语义代理，将多个数据库中的结构化数据基于现有词汇体系进行映射与关联。该方法在关系型数据库表层构建语义层，采用多LLM智能体系统将表与列映射至Schema.org标准术语。在多个领域测试中，映射准确率超过90%，显著提升数据集成效率，助力企业释放数据价值。
原文摘要 · Abstract (English)
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.
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