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Top1. Introduction
The continuous innovations in web technologies are rapidly changing smart systems by enabling real-time monitoring using smart devices, thus promoting a convenient and flexible lifestyle, improve communication, reducing operational cost, and many more (Esposito et al., 2018; Itria et al., 2017; Pateraki et al., 2020). The smart system implemented in a smart home, smart city, smart agriculture, smart healthcare are intelligent systems that make use of modern technologies such as IoT, semantic web, big data, cloud computing, advanced analytics with deep learning to improve the quality of lives efficiently (Park et al., 2017; Singhal, Saxena, Mittal, Dabas, & Kaur, 2021). These systems use Mobile Web Service (MWS) to provide users with the required functionalities. MWS are lightweight, self-contained modular applications that require adaptable protocols, smaller message formats suitable for resource-constrained IoT devices (Elgazzar, Martin, & Hassanein, 2016). The self-adaptive MWS discovery assesses changes in the situation and defines alternative solutions at whatever point the assessment demonstrates that the MWS has failed according to its functional and quality values, and/or the MWS does not fit the required context.
Context is any information that characterizes the situation of an entity (Verma & Srivastava, 2018). It is a key distinguishable feature among similar MWS and MWS consumers (Bouguettaya et al., 2017). It also facilitates the delivery of personalized MWS in smart agriculture, smart healthcare, smart home, etc. (Ben Njima, Gamha, Ghedira Guegan, & Ben Romdhane, 2019; Pradeep & Krishnamoorthy, 2019). Ontology supports the formal representation of various context knowledge and enables the sharing of information through logical reasoning, detection of inconsistencies, and simplify functional complexities. An ontology is a machine-readable and precise representation of rigorous conceptual schema (relevant entities, properties, relations, and rules) that is derived from consensus to capture meaning (semantics) within the domain of discourse (Pradeep & Krishnamoorthy, 2019). In the modeling of context for smart systems, ontology is widely adopted (Cabrera, Franch, & Marco, 2017). This is due to the modularity outlook in which several entities of smart applications such as MWS, user, location, etc. form the high-level ontology that can be extended to domain-specific concepts hierarchically.
Smart systems such as smart homes, smart cities, smart agriculture, smart healthcare, etc. are intelligent systems that require a high transmission rate and lower delay in real-time (Salman, Khalaf, & Abdulsahib, 2019). This leads to the generation of massive context information that necessitates proper representation, intelligent analytics to enable appropriate decisions in smart systems (Khalaf, Abdulsahib, Kasmaei, & Ogudo, 2020). The large and heterogeneous knowledge problem in smart systems hinders the delivery of the relevant MWS to right users at the right time. This is because the values of relevant context parameters that are obtained from multiple sources (diverse mobile devices, distributed users, constantly changing environment) are sometimes inconsistent, incomplete, or missing (Khalaf & Abdulsahib, 2019; Matassa & Riboni, 2020) Moreover, the use of limited contextual information to make a generalized decision hinder the delivery of the relevant MWS to the smart system user. The use of predefined contexts with arbitrary values in smart systems is inappropriate as it is hard to foresee all the conceivable circumstances emerging in a dynamic mobile environment (DME) (Cabrera et al., 2017). The lack of expressive, adaptable, consistent, and coherent context ontology designed specifically for self-adaptive MWS discovery hinders the delivery of the relevant MWS in a smart system.