BibSonomyburst/bibtex/1caa138967a9e3b4a5f9310d93ae20536BibSonomy RSS feed for /bibtex/1caa138967a9e3b4a5f9310d93ae205362012-02-17T16:30:20+01:00http://www.bibsonomy.org/bibtex/2e6eec4031fa845e3642ecc5543c03611/utahellutahell2011-10-02T14:55:10+02:00mining ontology <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 ESWC 2008, </em><em>volume 5021 of Lecture Notes in Computer Science, </em><em>Tenerife, Spain, </em><em>Springer Verlag, </em>(<em>June 2008</em>)Sun Oct 02 14:55:10 CEST 2011Tenerife, SpainProceedings of the 5th European Semantic Web Conference (ESWC 2008)JuneLecture Notes in Computer ScienceQuery Answering and Ontology Population: an Inductive Approach50212008mining ontology 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/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/2656c788e472b9fb9febf4d830250e56f/dblpdblp2008-05-27T00:00:00+02:00dblp <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>ESWC, </em><em>volume 5021 of Lecture Notes in Computer Science, </em><em>page 288-302. </em><em>Springer, </em>(<em>2008</em>)Tue May 27 00:00:00 CEST 2008ESWCconf/esws/2008288-302Lecture Notes in Computer ScienceQuery Answering and Ontology Population: An Inductive Approach.50212008dblp dblp