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A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching

A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching

Xingsi Xue, Junfeng Chen
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 14
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781522544852|DOI: 10.4018/IJSIR.2018040101
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MLA

Xue, Xingsi, and Junfeng Chen. "A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching." IJSIR vol.9, no.2 2018: pp.1-14. http://doi.org/10.4018/IJSIR.2018040101

APA

Xue, X. & Chen, J. (2018). A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching. International Journal of Swarm Intelligence Research (IJSIR), 9(2), 1-14. http://doi.org/10.4018/IJSIR.2018040101

Chicago

Xue, Xingsi, and Junfeng Chen. "A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching," International Journal of Swarm Intelligence Research (IJSIR) 9, no.2: 1-14. http://doi.org/10.4018/IJSIR.2018040101

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Abstract

This article describes how with the advent of sensors for collecting environmental data, many sensor ontologies have been developed. However, the heterogeneity of sensor ontologies blocks semantic interoperability between them and limits their applications. Ontology matching is an effective technique to solve the problem of sensor ontology heterogeneity. To improve the quality of sensor ontology alignment, the authors propose a semiautomatic ontology matching technique based on a preference-based multi-objective evolutionary algorithm (PMOEA), which can utilize the user's knowledge of the solution's quality to direct MOEA to effectively match the heterogeneous sensor ontologies. The authors specifically construct a new multi-objective optimal model for the sensor ontology matching problem, propose a user preference-based t-dominance rule, and design a PMOEA to solve the sensor ontology matching problem. The experimental results show that their approach can significantly improve the sensor ontology alignment's quality under different heterogeneous situations.

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