Discovery Mechanism for Learning Semantic Web Service

Discovery Mechanism for Learning Semantic Web Service

Chaker Ben Mahmoud (IResCoMath, Gabes, Tunisia), Ikbel Azaiez (IResCoMath, Gabes, Tunisia), Fathia Bettahar (IResCoMath, Gabes, Tunisia) and Faiez Gargouri (ISIMSfax, Sfax, Tunisia)
Copyright: © 2016 |Pages: 21
DOI: 10.4018/IJSWIS.2016010102
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Abstract

Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.
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2. Background

Semantic Web services (SWS) represent an extension of the existing Web services technology. They expand the Web from a source of distributed information to a source of distributed services, where software resources can be assembled hurriedly to satisfy user’s request (McIlraith et al, 2001). Thus, SWS are used to describe Web services with semantic content so that service discovery, composition, and invocation can be done automatically. For example, intelligent agents capable of processing the semantic information provided can be used.

The process of Web development until the onset of SWS is illustrated in Figure 1 (Fensel et al, 2002).

Figure 1.

The evolution of the semantic web

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