A Multi-Agent System for Semantic Mapping of Relational Data to Knowledge Graphs

2025-11-09

AI Summary

Enterprise multi-source heterogeneous databases engender data silos and impede semantic interoperability. To address this, we propose a multi-agent collaborative semantic mapping framework wherein large language models serve as semantic agents to automatically align relational database tables and columns with the Schema.org standard ontology, thereby constructing a unified semantic abstraction layer. The framework integrates knowledge graphs, Schema.org-based semantic annotation, and a multi-agent architecture to achieve over 90% mapping accuracy across diverse domains. Compared with conventional ETL pipelines or manual mapping approaches, our method significantly improves efficiency and scalability in cross-system data integration. It provides a practical, scalable technical pathway for large-scale enterprise semantic interoperability, enabling robust, ontology-driven data unification without extensive human curation.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Agentic search
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

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.

Problem

• Mapping relational databases to knowledge graphs using semantic agents
• Integrating siloed enterprise data through vocabulary-based mappings
• Applying large language models for cross-system structured data interoperability

Innovation

• Multi-agent system maps relational data semantically
• LLM agents utilize Schema.org for table mapping
• Method achieves over 90% mapping accuracy