Matchmaking in e-marketplaces consists of finding and retrieving promising counterparts for a given request from the set of available advertisements. This paper proposes the use of nonmonotonic inferences (concept contraction and concept abduction) in a semantic-matchmaking process for ranking resource descriptions. Concept contraction can be used to amend requests incompatible with the resource descriptions. The more amendments needed, the less is the degree of match. If a request is compatible with an advertisement but does not subsume it, concept abduction can be used to hypothesize extra features in the advertisement. The more it is necessary to hypothesize, the less is the degree of match. These basic ideas are utilized to compute a meaningful matchmaking ranking. Using logical explanations on matchmaking results, an approach and algorithms are proposed for the progressive refinement and revision of requests, up to an almost exact match. The related issue of user interaction is also tackled, and a us...

We propose a framework and tractable algorithms for semantic-based automated Web service composition, fully compliant with Semantic Web technologies. The approach exploits the recently proposed Concept Abduction inference service in Description Logics to extend Concept covering definition to expressive logics, and to solve Concept Covering problems in a significant subset of OWL-DL. We show how the proposed approach also deals with not-exact solutions, computing an approximate composition and providing an explanation of which part of the request is not covered by the composite service. We present the formalization of the approach, the proposed algorithms, a prototype system implementing the approach, and illustrate experiments carried out with it.