Visualizing logical dependencies in SWRL rule bases

Authors:
Saeed Hassanpour;Martin J. O'Connor;Amar K. Das
Affiliations:
Stanford Center for Biomedical Informatics Research, Stanford, CA;Stanford Center for Biomedical Informatics Research, Stanford, CA;Stanford Center for Biomedical Informatics Research, Stanford, CA
Venue:
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
Year:
2010

Citing 7
Cited 0

Introduction to Algorithms

Introduction to Algorithms
SAMOS in hindsight: experiences in building an active object-oriented DBMS

Information Systems
Visualization of Proofs in Defeasible Logic

RuleML '08 Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web
Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules

RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
ChronoMedIt - A computational quality audit framework for better management of patients with chronic conditions

Journal of Biomedical Informatics
Anomalies in ontologies with rules

Web Semantics: Science, Services and Agents on the World Wide Web
Visualizing defeasible logic rules for the semantic web

ASWC'06 Proceedings of the First Asian conference on The Semantic Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rule bases are common in many business rule applications, clinical decision support programs, and other types of intelligent systems. As the size of the rule bases grows and the interrelationships between rules become more complex, understanding dependencies among rules can be quite difficult. To address this challenge, we propose a novel approach for modeling logical dependencies among rules and for discovering patterns based on these dependencies. Our method uses rules bases written in the Semantic Web Rule Language (SWRL); we exploit SWRL's logical relationship with OWL to incorporate these semantics in our analysis. We couple this analysis with visualization techniques that create a rule dependency graph. We group nodes into layers based on their dependencies and cluster nodes within a layer if they have similar dependencies. We have evaluated our approach by applying it to two independently developed, publicly available ontologies containing SWRL rules. We show how our analysis and visualization approach can allow users to quickly examine patterns of logical relationships in such rule bases.