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

Abstract

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 determine 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.

Recommender Systems and Linked Open Data

“…Besides these user-generated constraints, as stated before, the system may also be aware of other constraint that are specific of the knowledge domain such as "if the house has a big garden then it cannot be in the city center ". We basically have two main types of knowledge-based recommender systems: case-based [13] and constraint-based [62,24,19] depending on the approach adopted in the representation and reasoning with user requirements and domain knowledge.…”

A semantic-based fully visual application for matchmaking and query refinement in B2C e-marketplaces

“…If D and S are in potential match, the characteristics B (for bonus) [13] specified in S but not requested in D represent the knowledge that can be elicited and proposed to the requester in order to still refine the initial query. At this point it should be easy to see how B can be computed solving a Concept Abduction problem.…”