Ontology Matching with Knowledge Rules
Shangpu Jiang, Daniel Lowd, Dejing Dou

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…
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Taxonomy
TopicsSemantic Web and Ontologies · Biomedical Text Mining and Ontologies · Service-Oriented Architecture and Web Services
