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Clustering Rule Bases Using Ontology-Based Similarity Measures

23 Pages Posted: 24 Jun 2018 Publication Status: Accepted

See all articles by Saeed Hassanpour

Saeed Hassanpour

Stanford University - Center for Biomedical Informatics Research

Martin J. O’Connor

Stanford University - Center for Biomedical Informatics Research

Amar K. Das

Stanford University - Center for Biomedical Informatics Research

Abstract

Rules are increasingly becoming an important form of knowledge representation on the Semantic Web. There are currently few methods that can ensure that the acquisition and management of rules can scale to the size of the Web. We previously developed methods to help manage large rule bases using syntactical analyses of rules. This approach did not incorporate semantics. As a result, rule categorization based on syntactic features may not be effective. In this paper, we present a novel approach for grouping rules based on whether the rule elements share relationships within a domain ontology. We have developed our method for rules specified in the Semantic Web Rule Language (SWRL), which is based on the Web Ontology Language (OWL) and shares its formal underpinnings. Our method uses vector space modeling of rule atoms and an ontology based semantic similarity measure. We apply a clustering method to detect rule relatedness, and we use a statistical model selection method to find the optimal number of clusters within a rule base. Using three different SWRL rule bases, we evaluated the results of our semantic clustering method against those of our syntactic approach. We have found that our new approach creates clusters that better match the rule bases’ logical structures. Semantic clustering of rule bases may help users to more rapidly comprehend, acquire, and manage the growing numbers of rules on the Semantic Web.

Keywords: Rule Management, Rule Categorization, Semantic Similarity, SWRL, OWL

Suggested Citation

Hassanpour, Saeed and O’Connor, Martin J. and Das, Amar K., Clustering Rule Bases Using Ontology-Based Similarity Measures (March 2014). Journal of Web Semantics First Look, Available at SSRN: https://ssrn.com/abstract=3199086 or http://dx.doi.org/10.2139/ssrn.3199086

Saeed Hassanpour

Stanford University - Center for Biomedical Informatics Research ( email )

1265 Welch Road
Stanford, CA
United States

Martin J. O’Connor

Stanford University - Center for Biomedical Informatics Research ( email )

1265 Welch Road
Stanford, CA
United States

Amar K. Das (Contact Author)

Stanford University - Center for Biomedical Informatics Research ( email )

1265 Welch Road
Stanford, CA
United States

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