Matchmaking in semantically enhanced web services: inductive ranking methodology

Abstract

Information is the most valuable asset of today’s world, both from the perspective of industry and academia. Accumulation of information on web based mediums with increasing demand of integration between different but necessarily collaborating parties drives today’s information management policies be service-driven. Semantic enhancements on web services take one more step ahead in this amazing evolution and improve integration between different applications besides removing concerns on scalability. Each emerging technology is a result of a trade-off and each trade-off yields new technologies to emerge. Once semantic web services become widely accepted, discovering the most appropriate and desired service becomes a challenging issue. Discovery is one of the key steps in the integration of applications, not only because it figures out who the companion in the interaction is but also it forms up the basis step for composition. Once discovery of web services can be successfully managed, composition of services will yield more successful results. To identify which service is the most appropriate for given user requirements, there has to be a valid, efficient and sensitive scoring mechanism to measure appropriateness. In this research, we focus on ranking of semantically enhanced web services. We base our strategy on individual relations between different concepts of ontology and identify the similarity degree with appropriate combination of those individual relations. We run our algorithm as well as other approaches addressing the same problem on a sample set of semantic web services and observe improvements on service discovery performance.