OWLS-MX: A Hybrid Semantic Web Service Matchmaker for OWL-S Services

Abstract

In this paper, we describe the first hybrid semantic Web service matchmaker for OWL-S services, called OWLS-MX. It complements crisp logic-based semantic matching of OWL-S services with token-based syntactic similarity measurements in case the former fails. The results of the experimental evaluation of OWLS-MX provide strong evidence for the claim that logic-based semantic matching of OWL-S services can be significantly improved by incorporating non-logic-based information retrieval techniques. An additional analysis of false positives and false negatives of the hybrid matching filters of OWLS-MX led to an even further improved matchmaker version called OWLS-MX2.

Semantic Web Service Search: A Brief Survey

“…Its logic-based semantic matching of services relies on the computation of strict and approximated logical I/O concept subsumption relations, and the logical specification plugin relation. Like its predecessor OWLS-MX3 [29], it also performs non-logicbased semantic matching with different classical tokenbased text similarity measures, as well as ontologybased structural matching of signature annotation concepts. Finally, it learns how to best aggregate the results of its matching filters by use of a binary SVM relevance…”

“…I/O P/E logic non-logic adaptive iSeM (2012) [30] OWL-S OWLS-MX3 (2012) [29] OWL-S SPARQLent (2010) [55] OWL-S SeMa 2 (2012) [46] OWL-S OWLS-SLR (2010) [47] OWL-S XSSD (2013) [42] OWL-S OWLS-iMatcher (2008) [23] OWL-S OPOSSUM (2007) [59] OWL-S Nuwa (2012) [25] OWL-S, SAWSDL SAWSDL-iMatcher (2011) [65] SAWSDL LOG4SWS (2012) [40] SAWSDL COV4SWS (2012) [ [20] WSML XAM4SWS (2010) [41] hRESTS, microWSMO PSemMa (2012) [5] prop. MDSM (2008) [34] PIM4SWS (meta-model) ( ) iServe (2010) [51] MSM (meta-model) classifier with an evidential coherence-based weighting scheme.…”

Humans, semantic services and similarity: A user study of semantic Web services matching and composition

“…This approach augments logic-based methods with other matching methods, especially from the information retrieval (IR) field. Works such as LARKS by Sycara et al [20], OWLS-MX by Klusch et al [1], iSPARQL by Kiefer et al [21] and Stroulia and Wang [22] introduce methods that combine text-based similarity, type signature matching, and logic-based matching of concept classes. Experimental evaluations done by Mikhaiel and Stroulia [23] and by Klusch et al [24] show that hybrid approaches outperform logic-based approaches, and that using a multitude of methods improves the performance of the matchers.…”

“…Two promising technologies in service orientation are service matching, which facilitates discovery of services on the Web [1,2,3], and service composition, which aims to assemble services into new applications [4,5,6,7,2,8,3]. These technologies, henceforth collectively referred to as service retrieval, promise to help users find and reuse new services, providing an agile and trustworthy environment for executing services and creating new applications.…”

A Model for Automated Service Composition System in SOA Environment

“…In many cases only one aspect is considered. For example [9] focuses on services selection based only on one functional requirement at a time. [6,14,15,16,17] show that non-functional requirements are considered to be of a key importance, however many approaches ignore the aspect of building a proper structure of a complex service which is key to optimization of i.e.…”