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

TL;DR

This paper introduces a formal framework and algorithms for creating and maintaining materialized RDB2RDF views in enterprise knowledge graphs, enhancing data integration and query performance.

Contribution

It presents a novel formal framework and architecture for incremental maintenance of RDB2RDF views in enterprise knowledge graphs.

Findings

Framework for constructing RDB2RDF views

Algorithms for incremental view maintenance

Improved query performance and data consistency

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.