BibSonomy publications for /user/eswc2008/logicWed May 28 14:50:01 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSQuery Answering and Ontology Population: an Inductive Approach2008similalrity inductive learning unswering uncertainty ontology logic description population measure query 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 \emph{k-Nearest Neighbor} algorithm for Description Logic KBs. It is based on the exploitation of an \emph{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.Wed May 28 14:49:54 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSRestricting and forgetting in DL-Lite2008dl-lite restricting ontology description forgetting logic formal-languages-2 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.Wed May 28 14:49:51 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSModule Extraction and Incremental Classification: A Pragmatic Approach for EL+ Ontologies2008ontology logic description extraction classification module incremental formal-languages-1 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.