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A pragmatic approach for the semantic description and matching of pervasive resources

A pragmatic approach for the semantic description and matching of pervasive resources Purpose – There has been an increased interest in the use of semantic description and matching techniques, to support service discovery and to overcome the limitations in the traditional syntactic approaches. However, the existing semantic matching approaches lack certain desirable properties that must be present in an effective solution to support service discovery. The purpose of this paper is to present a solution to facilitate the effective semantic matching of resource requests and advertisements in pervasive environments. Design/methodology/approach – The paper presents a semantic description and matching approach to facilitate resource discovery in pervasive environments; the approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request. Findings – The solution has been evaluated for its effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. The solution was also evaluated for its efficiency/scalability and from the experimental results obtained, it can be observed that for most practical situations, matching time can be considered acceptable for reasonable numbers of advertisements and request sizes. Originality/value – The proposed approach improves existing semantic matching solutions in several key aspects. Specifically; it presents an effective approximate matching and ranking criterion and incorporates priority consideration in the matching process. As shown in the evaluation experiments, these features significantly improve the effectiveness of semantic matching. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Pervasive Computing and Communications Emerald Publishing

A pragmatic approach for the semantic description and matching of pervasive resources

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Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
1742-7371
DOI
10.1108/17427371011033271
Publisher site
See Article on Publisher Site

Abstract

Purpose – There has been an increased interest in the use of semantic description and matching techniques, to support service discovery and to overcome the limitations in the traditional syntactic approaches. However, the existing semantic matching approaches lack certain desirable properties that must be present in an effective solution to support service discovery. The purpose of this paper is to present a solution to facilitate the effective semantic matching of resource requests and advertisements in pervasive environments. Design/methodology/approach – The paper presents a semantic description and matching approach to facilitate resource discovery in pervasive environments; the approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request. Findings – The solution has been evaluated for its effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. The solution was also evaluated for its efficiency/scalability and from the experimental results obtained, it can be observed that for most practical situations, matching time can be considered acceptable for reasonable numbers of advertisements and request sizes. Originality/value – The proposed approach improves existing semantic matching solutions in several key aspects. Specifically; it presents an effective approximate matching and ranking criterion and incorporates priority consideration in the matching process. As shown in the evaluation experiments, these features significantly improve the effectiveness of semantic matching.

Journal

International Journal of Pervasive Computing and CommunicationsEmerald Publishing

Published: Apr 6, 2010

Keywords: Semantics; Computer theory; Programming and algorithm theory

References