Knowledge elicitation for query refinement in a semantic-enabled e-marketplace

In this paper we present a knowledge-based approach to the elicitation of information from advertisements, in the framework of a semantic-enabled marketplace. The elicited information can be used for advertisements enriching and refining, without requiring users thorough knowledge of the domain. and to deter-mine a logicbased exact match. The approach exploits non-standard inference services in Description Logics, namely Abduction and Contraction, to tackle a typical problem of semantic-enabled marketplaces, that is the difficulty the average or casual user has in exploiting all the knowledge expressed in an e-commerce domain, which appears necessary to issue requests. We present an algorithm, which returns the set of concepts not included in the request - that can be used for query refinement - and more interesting what is still missing for each available supply, to obtain an exact. bidirectional, match.