Castano, S.; Ferrara, A.; Lorusso, D.; Näth, T. H. & Moeller, R.: Mapping Validation by Probabilistic Reasoning. In: Hauswirth, M.; Koubarakis, M. & Bechhofer, S. (Hrsg.): Proceedings of the 5th European Semantic Web Conference. Berlin, Heidelberg: Springer Verlag, 2008LNCS
[Volltext]
In the semantic web environment, where two or more independent ontologies can be used in order to describe knowledge and data, ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often, mappings are not derived by the semantics of the ontologies that are compared, but, rather, by an evaluation of the similarity of the terminology used in the two ontologies or of their syntactic structure. Moreover, ontology mappings can be inaccurate, because ontology matching tools derive such mappings from inaccurate terminology or even because they are not specifically tailored for the domain at hand. In this paper, we propose a new mapping validation approach for interpreting similarity-based mappings as semantic relations, by coping also with inaccuracy situations. The idea is to see two independent ontologies as a unique distributed knowledge base and to assume a semantic interpretation of ontology mappings as probabilistic and hypothetical relations among ontology elements. We present and use a probabilistic reasoning tool in order to validate mappings and to possibly infer new relations among the ontologies.

d'Amato, C.; Fanizzi, N. & Esposito, F.: Query Answering and Ontology Population: an Inductive Approach. In: Hauswirth, M.; Koubarakis, M. & Bechhofer, S. (Hrsg.): Proceedings of the 5th European Semantic Web Conference. Berlin, Heidelberg: Springer Verlag, 2008LNCS
[Volltext]
In the context of Semantic Web, deductive reasoning is used for making explicit the implicit knowledge of a knowledge base (KB). Anyway, purely logic-based approaches can fail when data comes from distributed sources, where contradictions usually turn out. Inductive instance-based learning methods can be effectively used in such a case, since they are well known to be efficient and fault tolerant. In this paper we propose an inductive method for improving the concept retrieval and for the performing the ontology population in a (semi-)automatic way. By casting concept retrieval to a classification problem with the goal of assessing the individual memberships w.r.t. the query concepts, we propose an extension of the k-Nearest Neighbor algorithm for Description Logic KBs. It is based on the exploitation of an entropy-based dissimilarity measure. The procedure retrieves individuals belonging to query concepts, by analogy with other training instances, on the grounds of the classification of the nearest ones w.r.t. the dissimilarity measure. We experimentally show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.

Kourtesis, D. & Paraskakis, I.: Combining SAWSDL, OWL-DL and UDDI for Semantically Enhanced Web Service Discovery. In: Hauswirth, M.; Koubarakis, M. & Bechhofer, S. (Hrsg.): Proceedings of the 5th European Semantic Web Conference. Berlin, Heidelberg: Springer Verlag, 2008LNCS
[Volltext]
UDDI registries are included as a standard offering within the product suite of any major SOA vendor, serving as the foundation for establishing design-time and run-time SOA governance. Despite the success of the UDDI specification and its rapid uptake by the industry, the capabilities of its offered service discovery facilities are rather limited. The lack of machine-understandable semantics in the technical specifications and classification schemes used for retrieving services, prevent UDDI registries from supporting fully automated and thus truly effective service discovery. This paper presents the implementation of a semantically-enhanced registry that builds on the UDDI specification and augments its service publication and discovery facilities to overcome the aforementioned limitations. The proposed solution combines the use of SAWSDL for creating semantically annotated descriptions of service interfaces and the use of OWL-DL for modelling service capabilities and for performing matchmaking via DL reasoning.

Rosati, R.: Finite model reasoning in DL-Lite. In: Hauswirth, M.; Koubarakis, M. & Bechhofer, S. (Hrsg.): Proceedings of the 5th European Semantic Web Conference. Berlin, Heidelberg: Springer Verlag, 2008LNCS
[Volltext]
The semantics of OWL-DL and its subclasses are based on the classical semantics of first-order logic, in which the interpretation domain may be an infinite set. This constitutes a serious expressive limitation for such ontology languages, since, in many real application scenarios for the Semantic Web, the domain of interest is actually finite, although the exact cardinality of the domain is unknown. Hence, in these cases the formal semantics of the OWL-DL ontology does not coincide with its intended semantics. In this paper we start filling this gap, by considering the subclasses of OWL-DL which correspond to the logics of the DL-Lite family, and studying reasoning over finite models in such logics. In particular, we mainly consider two reasoning problems: deciding satisfiability of an ontology, and answering unions of conjunctive queries (UCQs) over an ontology. We first consider the description logic DL-Lite_R and show that, for the two above mentioned problems, finite model reasoning coincides with classical reasoning, i.e., reasoning over arbitrary, unrestricted models. Then, we analyze the description logics DL-Lite_F and DL_Lite_A. Differently from DL-Lite_R, in such logics finite model reasoning does not coincide with classical reasoning. To solve satisfiability and query answering over finite models in these logics, we define techniques which reduce polynomially both the above reasoning problems over finite models to the corresponding problem over arbitrary models. Thus, for all the DL-Lite languages considered, the good computational properties of satisfiability and query answering under the classical semantics also hold under the finite model semantics. Moreover, we have effectively and easily implemented the above techniques, extending the DL-Lite reasoner QuOnto with support for finite model reasoning.

Suntisrivaraporn, B.: Module Extraction and Incremental Classification: A Pragmatic Approach for EL+ Ontologies. In: Hauswirth, M.; Koubarakis, M. & Bechhofer, S. (Hrsg.): Proceedings of the 5th European Semantic Web Conference. Berlin, Heidelberg: Springer Verlag, 2008LNCS
[Volltext]
The description logic EL+ has recently proved practically useful in the life science domain with presence of several large-scale biomedical ontologies such as SNOMED CT. To deal with ontologies of this scale, standard reasoning of classification is essential but not sufficient. The ability to extract relevant fragments from a large ontology and to incrementally classify it has become more crucial to support ontology design, maintenance and re-use. In this paper, we propose a pragmatic approach to module extraction and incremental classification for EL+ ontologies and report on empirical evaluations of our algorithms which have been implemented as an extension of the CEL reasoner.

Wang, Z.; Wang, K.; Topor, R. & Pan, J. Z.: Restricting and forgetting in DL-Lite. In: Hauswirth, M.; Koubarakis, M. & Bechhofer, S. (Hrsg.): Proceedings of the 5th European Semantic Web Conference. Berlin, Heidelberg: Springer Verlag, 2008LNCS
[Volltext]
Description logics form the foundation of ontologies used in the Semantic Web. To support reuse and integration of ontologies in Semantic Web applications, it is often necessary to restrict ontologies to a subset of their concepts and roles, or equivalently to forget a complementary subset of concepts and roles from the ontologies. We present the first detailed account of this problem for description logics, in particular for the DL-Lite family of description logics. Specifically, we present a semantic definition of forgetting that generalises the standard definition for classical logic. We introduce algorithms for forgetting concepts roles from both DL-Lite TBoxes and ABoxes. We prove the algorithms are sound and complete with respect to the semantics, and demonstrate how they can be used to speed-up query answering in DL-Lite knowledge bases.