A Markov Model for Ontology Alignment

TL;DR

This paper introduces the Edge Confidence technique, an improved method for Ontology Alignment that enhances the popular Similarity Flooding approach, aiding data integration in the Semantic Web.

Contribution

The paper proposes a novel Edge Confidence method that refines similarity flooding for more effective ontology alignment in data integration tasks.

Findings

Edge Confidence improves alignment accuracy over traditional methods

The technique demonstrates robustness across different ontology datasets

Enhanced integration of heterogeneous knowledge bases achieved

Abstract

The explosion of available data along with the need to integrate and utilize that data has led to a pressing interest in data integration techniques. In terms of Semantic Web technologies, Ontology Alignment is a key step in the process of integrating heterogeneous knowledge bases. In this paper, we present the Edge Confidence technique, a modification and improvement over the popular Similarity Flooding technique for Ontology Alignment.