Abstract
In this paper, we present a fine-grained matching method of the services based on a hybrid similarity measure. We propose a novel encoding of the services descriptions, allowing the match between a request and an advertisement in order to make more efficient publishing and searching process of Web services and reduce the number of comparisons required. By this kind of similarity between concepts of profile, a precise matching method is developed to match the profile of the Web services and user. Searching process in the UDDI registry is done via an algorithm that allows us to extract the search concepts and retrieve the top-k services, thereby further reducing the search engine's response time. The approach is illustrated through some experiments both on real and synthetic data to demonstrate its consistency and effectiveness.
Highlights
The recent evolution of Internet, driven by the SWS (Semantic Web Services) technology, has extended the role of the Web from a support of information interaction to a middleware for B2B (Business to Business) interactions
Semantic Web services allow a homogeneous use of heterogeneous software components deployed in large networks and in particular the Internet
It is capable of integrating the profile into the process of semantic Web services discovery
Summary
The recent evolution of Internet, driven by the SWS (Semantic Web Services) technology, has extended the role of the Web from a support of information interaction to a middleware for B2B (Business to Business) interactions. Service computing tries to solve questions based on profiles of users from a contextual informational view of the Web where users have several characteristics such as the client terminal, the client preferences, its location, etc All these parameters form a particular context of use called the profile. We are leveraging the profile information in a Web services discovery system during the search and selection stages by selecting services corresponding as well as possible to the user request. This selection is based on the use of similarity measures, which estimate the matching degree between the desired profile and the provided profile parameters.
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