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

2025-11-09

AI Summary

E-commerce platforms generate vast volumes of unstructured product data, exacerbating challenges in information retrieval and cross-system interoperability. To address this, we propose a large language model (LLM)-based multi-agent semantic mapping framework that enables automated, interpretable transformation from relational databases to knowledge graphs. The framework adopts Schema.org as a unified semantic ontology and orchestrates specialized agents to perform table- and column-level semantic annotation, schema alignment, and RDF triple generation—substantially reducing manual knowledge graph construction effort. Empirical evaluation across multiple business scenarios achieves over 90% semantic mapping accuracy, effectively supporting heterogeneous data integration and sharing. Our key contribution lies in the tight integration of LLM-driven semantic understanding with multi-agent collaboration, enabling—for the first time—the end-to-end, high-precision, and scalable automatic construction of product-domain knowledge graphs.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Statistical Relational/Logic Learning

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
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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

• Automates product knowledge graph construction from unstructured e-commerce data
• Addresses manual KG creation challenges using AI agents and LLMs
• Solves scalability issues in product data integration for retail applications

Innovation

• AI agent-driven automated knowledge graph construction
• LLM-based three-stage ontology creation and refinement
• Schema-free extraction from unstructured product descriptions