A Decentralized Framework for Semantic Web Services Discovery Using Mobile Agent

Seghir, Nadia Ben; Kazar, Okba; Rezeg, Khaled

doi:10.4018/ijitwe.2015100102

Cited by 13 publications

(3 citation statements)

References 18 publications

(21 reference statements)

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“…The experiment results showed that the algorithm had high recall and precision [5]. Seghir et al (2015) discussed the mapping Web service modeling ontology (WSMO) enhanced semantic Web service discovery based on machine learning. In order to make use of a large number of existing Web services libraries, the mapping from web service description language (WSDL) to OWL-S was created.…”

Section: Literature Reviewmentioning

confidence: 99%

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Exploration of Learning Service Discovery Algorithm based on Ontology

Peng

2018

Int. J. Emerg. Technol. Learn.

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To study the learning service discovery algorithm based on ontology, the introduction of bipartite graph theory is focused on. Based on the undergraduate technology, a learning service discovery algorithm based on the basic bipartite graph theory (eLSDA-BG) is proposed. The algorithm mainly converts the problem of learning service match into the optimal complete match problem of bipartite graphs. Through the optimal complete match problem, the learning service match is realized and the learning service discovery is finally reached. The eLSDA-BG algorithm is compared with the greedy algorithm, the UDDI (Universal Description, Discovery, and Integration) algorithm, and OWL-S (Web Ontology Language for Service) algorithm. The comparison of results shows that the eLSDA-BG algorithm proposed can improve recall ratio, accuracy rate and efficiency of learning service discovery in situation of certain sample size.

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Section: Literature Reviewmentioning

confidence: 99%

“…Another better ontology knowledge representation model is WSMO, which has a semantic framework for processing Web services, but lacks the extensibility of OWL-S. Therefore, a model that maps WSDL to WSMO was proposed [6]. Buvana and Suganthi (2015) developed a highly reliable and secure self-learning solution based on service.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploration of Learning Service Discovery Algorithm based on Ontology

Peng

2018

Int. J. Emerg. Technol. Learn.

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“…The migration decision can be initiated by the agent itself, independently; and mobility is thus controlled by the application and not by the execution system (ELFalou, 2006). Our interest in mobile agents has arisen for several reasons (Ben Seghir et al, 2015):…”

Section: Introductionmentioning

confidence: 99%

A semantic web services discovery approach based on a mobile agent using metadata

Seghir

Kazar

Rezeg

et al. 2017

IJICC

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Purpose The success of web services involved the adoption of this technology by different service providers through the web, which increased the number of web services, as a result making their discovery a tedious task. The UDDI standard has been proposed for web service publication and discovery. However, it lacks sufficient semantic description in the content of web services, which makes it difficult to find and compose suitable web services during the analysis, search, and matching processes. In addition, few works on semantic web services discovery take into account the user’s profile. The purpose of this paper is to optimize the web services discovery by reducing the search space and increasing the number of relevant services. Design/methodology/approach The authors propose a new approach for the semantic web services discovery based on the mobile agent, user profile and metadata catalog. In the approach, each user can be described by a profile which is represented in two dimensions: personal dimension and preferences dimension. The description of web service is based on two levels: metadata catalog and WSDL. Findings First, the semantic web services discovery reduces the number of relevant services through the application of matching algorithm “semantic match”. The result of this first matching restricts the search space at the level of UDDI registry, which allows the users to have good results for the “functional match”. Second, the use of mobile agents as a communication entity reduces the traffic on the network and the quantity of exchanged information. Finally, the integration of user profile in the service discovery process facilitates the expression of the user needs and makes intelligible the selected service. Originality/value To the best knowledge of the authors, this is the first attempt at implementing the mobile agent technology with the semantic web service technology.

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