BibSonomyburst/user/eswc2008/measureBibSonomy RSS feed for /user/eswc2008/measure2012-02-16T12:06:56+01:00http://www.bibsonomy.org/bibtex/20aedaf7d891b39d35f46baf40901c299/eswc2008eswc20082008-05-28T14:50:01+02:00similalrity inductive learning unswering uncertainty ontology logic description population measure query <span class="authorEditorList"><a href="/author/d&#039;Amato">Claudia d&#039;Amato</a>, <a href="/author/Fanizzi">Nicola Fanizzi</a>, and <a href="/author/Esposito">Floriana Esposito</a> </span><em>Proceedings of the 5th European Semantic Web Conference, </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em>(<em>June 2008</em>)Wed 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.http://www.bibsonomy.org/bibtex/21a97459b80d2cac3fd8b935452fe0418/eswc2008eswc20082008-05-28T14:50:01+02:00rdf measure clustering distance learning <span class="authorEditorList"><a href="/author/Grimnes">Gunnar Grimnes</a>, <a href="/author/Edwards">Peter Edwards</a>, and <a href="/author/Preece">Alun Preece</a> </span><em>Proceedings of the 5th European Semantic Web Conference, </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em>(<em>June 2008</em>)Wed May 28 14:50:01 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSDistance Based clustering of Semantic Web Resources2008rdf measure clustering distance learning The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue that an essential feature for such agents is the capability to analyse data and learn. In this paper we outline the challenges and issues surrounding the application of clustering algorithms to Semantic Web data. We present several ways to extract instances from a large RDF graph and computing the distance between these. We evaluate our approaches on three different data-sets, one representing a typical relational database to RDF conversion, one based on data from a ontologically rich Semantic Web enabled application, and one consisting of a crawl of FOAF documents; applying both supervised and unsupervised evaluation metrics. Our evaluation did not support choosing a single combination of instance extraction method and similarity metric as superior in all cases, and as expected the behaviour depends greatly on the data being clustered. Instead, we attempt to identify characteristics of data that make particular methods more suitable.