Abstract-There is a growing number of web services available on the Internet, providing a wide range of functionalities. This diversity introduces a variety of new challenges in the field of software engineering -service discovery, integration, and composition, all of which require, to some extent, "service matching". Web-service matching (or alignment) is the task of mapping the functionalities of two web services, assuming that these functionalities overlap somewhat.In this paper we propose a novel graph-theoretic approach, called Semantic Flow Matching (SFM), for matching REST web services, specified in WADL (Web Application Description Language). The method builds a heterogeneous network of WADL elements and semantically related terms, and uses this network to match similar functionalities of different web services. The method is implemented in a prototype tool that consists of two modules: a converter and a mapper; where the converter wraps the REST web services in WADL format and the mapper module matches web services based on their semantics extracted from the WADL interface build by the converter. We demonstrate the potential of the approach with a small case study.
Network association is a prevalent representation when dealing with data from present-day applications. Examples are crime event connections in criminology, cellphone call graphs in telecommunication, co-authorship networks in bibliometrics, etc. A large body of work has been devoted to the analysis of these networks and the discovery of their underlying structures. One important structure is the notion of community i.e. a group of nodes that are relatively cohesive within and reasonably disjointed outside. Finding the communities usually relies on a closeness/distance measure between network nodes. In this paper, we propose a novel closeness measure, named iCloseness, inspired by the theory of Diffusion of Innovations in anthropology. It is computed based on the intersection of neighbourhoods and quantifies the closeness of two nodes. To apply this measure we adjusted the Top Leaders community mining method to use this measure for community detection. Experimental results on real world and synthesized information networks show the effectiveness of our proposed measure and highly motivate the application of the iCloseness measure in the context of community mining.
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