Ontology Matching with Knowledge Rules

TL;DR

This paper introduces a novel ontology matching approach that leverages knowledge rules and probabilistic integration to improve alignment accuracy, especially for complex concepts, outperforming previous methods.

Contribution

It proposes a knowledge rule-based strategy integrated with existing methods using a probabilistic framework for enhanced ontology matching accuracy.

Findings

Achieves higher F-score than state-of-the-art methods

Effective in matching complex concepts

Improves accuracy by incorporating knowledge rules

Abstract

Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves substantially better F-score than the previous state-of-the-art on three ontology matching domains.