Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach

Abstract

Matchmaking arises when supply and demand meet in an electronic marketplace, or when agents search for a web service to perform some task, or even when recruiting agencies match curricula and job profiles. In such open environments, the objective of a matchmaking process is to discover best available offers to a given request.We address the problem of matchmaking from a knowledge representation perspective, with a formalization based on Description Logics. We devise Concept Abduction and Concept Contraction as non-monotonic inferences in Description Logics suitable for modeling matchmaking in a logical framework, and prove some related complexity results. We also present reasonable algorithms for semantic matchmaking based on the devised inferences, and prove that they obey to some commonsense properties.Finally, we report on the implementation of the proposed matchmaking framework, which has been used both as a mediator in e-marketplaces and for semantic web services discovery.

Humans, semantic services and similarity: A user study of semantic Web services matching and composition

“…Our study differs from [19] in two important aspects. First, we use a different methodology: rather than developing a theoretical similarity model and then evaluating it, we first found out empirically users' similarity preferences and then modeled them.…”

“…Our work is also related to non-monotonic service similarity of Di Noia et al [19], who found it to be more intuitive than text-based vector space model retrieval using an experimental evaluation of three test cases and 30 people. Our study differs from [19] in two important aspects.…”

T2.O.M. T.O.M.: Techniques and Technologies for an Ontology-Based Mobility Tool with Open Maps

“…where rankPotential (r, s) [5] is the semantic distance measuring the degree of correspondence between request r and point of interest annotation POI, computed solving the Concept Abduction Problem (CAP) [5]; rankPotential (s, ) is the maximum possible semantic distance w.r.t. axioms in the selected ontology.…”

“…Based on the formal semantics of such descriptions, an explanation of the matchmaking outcome is then provided to the user to foster further interaction. This is accomplished by using a lightweight version of non-standard inference algorithms, i.e., Concept Abduction, Concept Contraction and Bonuses Calculation [5,4].…”

RFID-based Semantic-enhanced Ubiquitous Decision Support System for Healthcare

“…In the approach we propose here, non-monotonic inferences presented in [5] are exploited to retrieve suitable treatments for a given disease taking into account the case history of the patient. The system will calculate a score, based on the semantic compatibility between diseases affecting the patient and characteristics of available drugs, so allowing to: (i) find possible inconsistencies in a proposed therapy; (ii) arrange best treatment options in relevance order; (iii) explain the matchmaking outcomes in both cases.…”