Efficient Web Service Query Approach Based on Concept Relaxation

Ou, Weijie; Zeng, Cheng; Xiang, X.; Peng, Zhiyong; Li, Deyi

doi:10.3724/sp.j.1016.2011.02381

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…The most widely used metadata are terms from the interface signature (i.e., the "Input" section and "Output" section) and/or the textual description of a Web service's capabilities. As interface signature provides a derived and concise description of the functions offered by the Web service, a straightforward mapping approach has been to extract a set of keywords or terms from the interface signature, then the service discovery system evaluates the relevance between sets associated to service and request (Bener et al, 2009;Plebani & Pemici, 2009;Ou et al, 2011;Farrag et al, 2013;Al Ridhawi & Karmouch, 2015). For instance, Plebani & Pemici (2009) evaluated similarity among service/request interfaces by using a bipartite graph matching approach.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Plebani & Pemici (2009) evaluated similarity among service/request interfaces by using a bipartite graph matching approach. Ou et al (2011) proposed a method of service query that matches the concept sets of the considered I/O interfaces. The idea of concept relaxation was combined with a unidirectional bipartite graph matching approach to measure the similarity of concept sets.…”

Section: Related Workmentioning

confidence: 99%

“…Methods several state-of-the-art Web service discovery methods including URBE (Plebani & Pemici, 2009), SimRSP (Ou et al, 2011), SDMA (Farrag et al, 2013), OWLS-MX (Kluschet al, 2009), SMARTSPACE (Dasgupta, S et al, 2014), and UltiMatch-NL (Mohebbiet al, 2014). Of these compared methods, four are similarity measurement methods: URBE, SimRSP, SDMA, and UltiMatch-NL.…”

Section: Metricsmentioning

confidence: 99%

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A semantic similarity measure integrating multiple conceptual relationships for web service discovery

Chen

et al. 2017

Expert Systems with Applications

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The process of Web service discovery identifies the most relevant services to requesters' service queries. We propose a new measure of semantic similarity integrating multiple conceptual relationships (SIMCR) for Web service discovery. The new measure enables more accurate service-request comparison by treating different conceptual relationships in ontologies such as is-a, has-a and antonomy differently. Each service or request is represented by vectors of terms (or words) that characterize both the interface signature and textual description. The overall semantic similarity is computed as a weighted aggregation of interface similarity and description similarity. The experimental results confirm the effectiveness of the proposed semantic similarity measure. As demonstrated in this study, the semantic Web service discovery method based on the proposed similarity measure outperforms existing state-of-the-art discovery methods in terms of precision, recall and Fmeasure. The proposed semantic similarity measure has wider applications such as to improve document classification or clustering, and to more accurately represent and apply knowledge in expert and intelligent systems.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

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A semantic similarity measure integrating multiple conceptual relationships for web service discovery

Chen

et al. 2017

Expert Systems with Applications

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A Novel Web Service Discovery Method Combining Semantic Interface Similarity and Context Similarity

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Web service discovery is to find the most relevant services to satisfy the requester queries by means of similarity matching between Web services and a service requirement. We propose a novel Web service discovery method based on semantic similarity measure combing I/O similarity and context similarity. A new similarity measurement between terms is put forward as the basis of Web service similarity. Similarity between terms is represented by the similarity of two concepts containing them, and is calculated by means of the length of the shortest path between two concepts. Three kinds of linked relations are considered, i.e. ISA, HASA and antonymy relations. The experiments demonstrate that the proposed approach can improve the accuracy of concept similarity measure. Service discovery based on the similarity measure has better query performance than previous discovery methods in the precision, recall and F-measure.

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Efficient Web Service Query Approach Based on Concept Relaxation

Cited by 2 publications

References 3 publications

A semantic similarity measure integrating multiple conceptual relationships for web service discovery

A semantic similarity measure integrating multiple conceptual relationships for web service discovery

A Novel Web Service Discovery Method Combining Semantic Interface Similarity and Context Similarity

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