A Nonmonotonic Approach to Semantic Matchmaking and Request Refinement in E-Marketplaces

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 user-friendly tool is presented that allows full utilization of the semantic-based query/revision/refinement process while completely hiding logical technicalities.