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.
Information incompleteness, or ignorance, is an issue that we have to consider in Semantic Web applications. Dempster-Shafer theory has been traditionally applied in information incompleteness situations. On the other hand, logic plays a major role in the Semantic Web community. In this paper, we propose a framework that applies Dempster-Shafer theory in a Description Logic Knowledge Base environment. We name our model a Dempster-Shafer DL Knowledge Base.
Developments in semantic web technologies have promoted ontological encoding of knowledge from diverse domains. However, modelling many practical domains requires more expressive representations schemes than what the standard description logics(DLs) support. We extend the DL SROIQ with constraint networks and grounded circumscription. Applications of constraint modelling include embedding ontologies with temporal or spatial information, while grounded circumscription allows defeasible inference and closed world reasoning. This paper overcomes restrictions on existing constraint modelling approaches by introducing expressive constructs. Grounded circumscription allows concept and role minimization and is decidable for DL. We provide a general and intuitive algorithm for the framework of grounded circumscription that can be applied to a whole range of logics. We present the resulting logic: GC-SROIQ(C), and describe a tableau decision procedure for it.
Modeling real world domains requires ever more frequently to represent uncertain information. The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability. In this paper we discuss approaches for performing inference from probabilistic ontologies following the DISPONTE semantics. We present the algorithm BUNDLE for computing the probability of queries. BUNDLE exploits an underlying Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is used for computing the probability of the query.
Recently several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. Most of these semantics rely on the notion of a repair, defined as an inclusion-maximal subset of the facts (ABox) which is consistent with the ontology (TBox). In this paper, we study variants of two popular inconsistency-tolerant semantics obtained by replacing classical repairs by various types of preferred repair. We analyze the complexity of query answering under the resulting semantics, focusing on the lightweight logic DL-Lite_R. Unsurprisingly, query answering is intractable in all cases, but we nonetheless identify one notion of preferred repair, based upon priority levels, whose data complexity is "only" coNP-complete. This leads us to propose an approach combining incomplete tractable methods with calls to a SAT solver. An experimental evaluation of the approach shows good scalability on realistic cases.