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Top1. Introduction
Over the last few years, the paradigm of service-oriented computing has received much attention in support of dynamic, low-cost and cross-organizational construction of distributed applications in heterogeneous environments (Joshi et al., 2014). The widespread adoption of service-oriented architecture has encouraged enterprises to change the application development ladder from a component-based approach to a service-based approach. For example, Yahoo, Facebook, Google and Amazon have made efforts to package information resources as Web services and created Web service registries to encourage searchability, interoperability and adoption of Web services (Fuzan Chen et al., 2015).
These Web services offer a wide range of functionality for data enhancement in e-commerce, utilities, communications, marketing and especially e-learning. However, employing Web services in this manner requires the careful selection and composition of appropriate Web services. Service discovery is a significant activity in the Services Computing paradigm (Bo Cheng et al., 2017).
Web services are self‐describing software applications that provide certain functionalities independently from underlying implementation technologies. They are the foundation for service‐oriented architecture (SOA) and prepare the ground for creating distributed applications over the Web (Mingzhe Fang et al., 2017).
This however revealed some limitations, namely, the lack of semantics in the description of the Web services standards and the ambiguity in the description of Web services specified by some semantic approaches. Furthermore, semantic requirements, such as the enrichment of the mechanisms of semantic annotation, which are revealed during discovery and composition, lead to improved discovery mechanisms. The answer to such semantic interoperability gaps has been found in the Semantic Web. The application of semantic web principles to web services makes it possible to use the meaning of the data exchanged to improve the feasibility of discovery and composition. Indeed, this aspect offers to Web services an explicit semantic description of their functionalities which would be comprehensible by software agents and human users. It also provides a correct interpretation of the information sent and received in the context of discovery and invocation, especially composition and orchestration (Berners-Lee et al., 2001).
In this context, several Semantic Web Service description frameworks have been developed, such as Ontology Web Language for Services (OWL-S), Semantic Annotation for WSDL (SAWSDL), Web Service Semantics (WSDL-S), and Web Service Modeling Ontology (WSMO). With well-defined semantic associations existing between terms organized in the ontology, the semantic Web service approach is capable of autonomous publication, discovery and execution of Web services at a semantic rather than syntactic level (Martin et al., 2004; Bournez et al., 2007; Ngan et al., 2013).
Another challenge in Web services is personalization. Most Web services are provided to different consumers in exactly the same way and are not designed and implemented for personalized service consumption. In many situations, the service selection requires taking account of the needs and preferences of individual consumers. Personalized Web services refer to those services that can be customized or tailored to the needs and preferences of individual service consumers (Abdelmalek Metrouh et al., 2018).
Indeed, this evolution of web services has not only affected the business sector but all other sectors, including the education sector, which have been forced to follow this trend. In learning systems, learners do not see e-learning as passive content deposits but rather dynamic environments that provide them with tools to support the entire learning process.
In a distributed environment, the diversity of data in different learning systems presents several challenges. First, the learning content must be reused in different environments. For this reason, it must be separated from the specific execution constraints. This is ensured by using web services. Secondly, these systems must find and identify a learning object using its metadata rather than its actual content. Hence, the need to add a semantic layer to these web services. Finally, learners' abilities to learn can vary widely, but learning systems still deliver generic learning objects to all learners. Therefore, the personalization and adaptation of learning resources can solve these problems.