A fuzzy framework for Semantic Web Service description, matchmaking, ranking and selection

A fuzzy framework for Semantic Web Service description, matchmaking, ranking and selection

Table of Contents


Semantic Web Services (SWSs) provide a semantic representation of web services that provides a well-defined semantics in order to make them computer-interpretable, use-apparent, and agent-ready. In the last years, it is becoming more and more accepted the idea that current Semantic Web technologies are not appropriate to deal with imprecise and vague knowledge, inherent to several real-world domains. This paper proposes a fuzzy logic-based framework that makes service description, discovery, and selection more flexible, and that includes support for non-functional properties. In particular, we extend OWL-S services by including a fuzzy domain ontology, which improves the definition of functional properties, and a fuzzy Quality of Service ontology, which improves the definition of non-functional properties. We also show how to enhance the tasks of Semantic Web Services (SWS) selection and ranking by means of fuzzy reasoning.


A web service is a software system which allows to access to a remote application through standard Web protocols. A major limitation of the Web services technology is that discovering and composing services requires a lot of manual effort. To solve this problem, some researchers proposed to extend Web services with ideas from the Semantic Web [2], an extension of the web to give information a well-defined meaning, enabling automatic information processing. The extension of Web services with a semantic description of their functionality is referred to as Semantic Web Services (SWSs) [6]. SWSs allow software agents to automatically discover, invoke, and compose services. With information described semantically, a service broker could satisfy a complex user request by searching a services registry, matching the most suitable service (or services) and, if necessary, composing the individual results to perform the full task. Semantic Web Services (SWS) descriptions rely on two kinds of ontologies. Firstly, a generic ontology specifies the main aspects of the service regardless of the application domain. OWL-S [21] is a language for SWS representation, being actually an OWL ontology to annotate Web Services for adding semantic information. Secondly, a domain ontology provides specific knowledge about the domain of the service. Domain knowledge is represented in an OWL or OWL 2 ontology [10], [20]. Due to the increasing number of services offering similar functionalities, non-functional properties (NFPs) have become essential criteria to enhance the processes of service selection. Thus, a service description usually includes functional properties and non-functional properties. Current approaches often use Quality of Service (QoS) to filter the SWSs discovery results based on user constraints of QoS descriptions [22], as services with equivalent functionality can be provided by different service providers with substantially different values of QoS. Nevertheless, different service providers and requesters may use different QoS concepts for describing service quality information, which leads to the issue of semantic interoperability of QoS. The solution is using a QoS ontology that supports not only describing QoS information in great detail but also facilitating various service participants expressing their QoS offers and demands at different levels of expectation [19]. During the last years, a lot of effort has been deployed to enhance current frameworks to describe NFP properties. For example, several QoS ontologies have been proposed [12], [14], [1], [19]. Today it is widely agreed that classical ontologies are not suitable to represent vague and imprecise knowledge that humans are very used to, such as the notions of cheap or small. In particular, the values of some non-functional properties of services, such as some QoS properties, cannot be properly described with crisp ontologies. In other words, because of the imprecise nature of the values of these properties, users cannot describe their request properly. As a solution, fuzzy ontologies [17] have been proposed as a combination of ontologies with techniques from fuzzy set theory and fuzzy logic [23]. Essentially, in fuzzy ontologies, fuzzy concepts denote fuzzy sets of individuals, whereas fuzzy roles denote fuzzy binary relations among individuals. Axioms are also extended to the fuzzy case and, consequently, it is possible to add an upper bound or a lower bound to some axioms. For instance, it is possible to represent fuzzy concepts inclusions, such as “fuzzyConcept1 is a subconcept of fuzzyConcept2 with at least degree 0.5”. The main objective of our research is to enhance Semantic Web Services (SWSs) by considering fuzzy SWS, where fuzzy ontologies are used rather than classical ontologies. More precisely, we use OWL-S as the SWS description language and extend both the functional and non-functional aspects of our new semantic web service framework with fuzzy ontologies. Then, we show how to enhance the tasks of SWS selection and ranking by means of fuzzy reasoning. As an additional contribution, we propose a framework for fuzzy ontology generation as a considerable extension of our previous work in [7]. This framework is used to generate the fuzzy domain ontologies for the fuzzy SWS, but can be used in other different frameworks.


This paper presents a fuzzy framework for Semantic Web Services (SWS) description, discovery, and selection. The purpose of this research is to expand the scope of the existing descriptions of Web services, enabling them to describe uncertain knowledge and to provide an integrated solution for fuzzy description-based service organization and matching. In particular, we extend the semantic description of the functional properties of Semantic Web Services (SWSs) by using a fuzzy domain ontology. We also enhance the semantic description of the non-functional properties by using fuzzy Quality of Service ontologies. Non-functional properties play an important role in Web service selection in order to evaluate and rank candidate Semantic Web Services (SWSs), so extending them by using fuzzy ontologies produces a more powerful formalism for Semantic Web Services (SWS) description. Up to now, we have shown that our approach may perform better in some cases. Future work will include a more rigorous evaluation of the fuzzy SWS selection, modifying more SWS in the OWL-S Service Retrieval Test Collection with fuzzy ontologies and non-functional properties.

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Ehsan Sharifi; Reza A. Moghadam ; Fernando Bobillo ; Mohamad M. Ebadzadeh



2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)






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Ehsan Sharifi has a Ph.D. in software engineering from Amirkabir University of Technology. His major research interests are software quality, software architecture and semantic web.