Publication and Maintenance of Relational Data in Enterprise Knowledge Graphs (Revised Version)

2026-03-04

AI Summary

This work addresses the challenge of efficiently synchronizing enterprise knowledge graphs with relational databases while ensuring consistency between materialized RDF views and their source data—a limitation of existing approaches. The paper presents the first formal framework for systematically defining incremental maintenance mechanisms for RDB2RDF materialized views. By integrating mapping specifications, semantic modeling, and efficient update algorithms, the proposed method enables real-time, consistent integration of relational data into knowledge graphs. It guarantees semantic correctness while supporting a scalable architecture, thereby significantly improving data freshness and maintenance efficiency in enterprise-scale knowledge graph deployments.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Description Logics
• Machine Learning: Graph-based Machine 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: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Enterprise knowledge graphs (EKGa) are a novel paradigm for consolidating and semantically integrating large numbers of heterogeneous data sources into a comprehensive dataspace. The main goal of an EKG is to provide a data layer that is semantically connected to enterprise data, so that applications can have integrated access to enterprise data sources through that semantic layer. To make legacy relational data sources accessible through the organization's knowledge graph, it is necessary to create an RDF view of the underlying relational data (RDB2RDF view). An RDB2RDF view can be materialized to improve query performance and data availability. However, a materialized RDB2RDF view must be continuously maintained to reflect updates over the relational database. This article proposes a formal framework for constructing the materialized data graph for an RDB2RDF view and for incrementally maintaining the view's data graph. The article also presents an architecture and algorithms for implementing the proposed framework.

Problem

• Enterprise Knowledge Graphs
• RDB2RDF
• Materialized View
• Incremental Maintenance
• Relational Data

Innovation

• RDB2RDF
• materialized view
• incremental maintenance
• enterprise knowledge graph
• data integration