A Semantic Scoring Approach for Service Offers

Abstract

Automating service selection using semantic approaches have been extensively studied in recent years. In fact, given the big number of provider offers, sourcing of the most relevant service to the client intentions is a complex task especially when providers and customers don't share the same knowledge degree. In particular, differentiating between very similar offers satisfying the same number of client constraints is still a challenging task. In this paper, we present a novel semantic scoring approach that helps clients to select the most appropriate service offer according to their intentions. Our approach detects direct and indirect semantic correspondences between these intentions and the available offers using ontological models. It fairly evaluates these offers and ranks them according to their semantic closeness to the client intentions taking into account both functional and QoS properties. Our ranking is based on a deep examination of provider offers and can distinguish between services that look the same for non expert clients.