BibSonomyburst/bibtex/1c091f27b3e3d70203862e2f3a1132928BibSonomy RSS feed for /bibtex/1c091f27b3e3d70203862e2f3a11329282012-02-17T09:37:37+01:00http://www.bibsonomy.org/bibtex/2fe32fd8af9387aadfb84f5ebb3f4bef5/utahellutahell2011-10-05T16:57:45+02:00mining ontology <span class="authorEditorList"><a href="/author/Kiefer">Christoph Kiefer</a>, <a href="/author/Bernstein">Abraham Bernstein</a>, and <a href="/author/Locher">André Locher</a> </span><em>Proceedings of the 5th European Semantic Web Conference ESWC 2008, </em><em> 5021, </em><em>page 478-492. </em><em>Tenerife, Spain, </em><em>Springer Verlag, </em>(<em>2008</em>)Wed Oct 05 16:57:45 CEST 2011Tenerife, SpainProceedings of the 5th European Semantic Web Conference (ESWC 2008)478-492Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods.50212008mining ontology Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods.http://www.bibsonomy.org/bibtex/2569e52ad749e5ea312aae2a76dca52d7/eswc2008eswc20082008-05-28T14:49:52+02:00sparql data statistical evaluation mining relational learning query-processing-1 <span class="authorEditorList"><a href="/author/Kiefer">Christoph Kiefer</a>, <a href="/author/Bernstein">Abraham Bernstein</a>, and <a href="/author/Locher">André Locher</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 14:49:52 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSAdding Data Mining Support to SPARQL via Statistical Relational Learning Methods2008sparql data statistical evaluation mining relational learning query-processing-1 In machine learning/data mining, people have been exploring how to learn models of relational data for a long time. The rational behind this is that exploiting the rich and complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. These links are usually hard to model by traditional propositional learning techniques. We extend this idea to the Semantic Web. In this paper we introduce a novel approach we call SPARQL-ML to perform data mining for Semantic Web data. Our approach is based on traditional SPARQL and statistical relational learning methods, such as Relational Probability Trees and Relational Bayesian Classifiers. We analyze our approach thoroughly conducting three sets of experiments on synthetic as well as real-world datasets. Our analytical results show that our approach can be used for any Semantic Web dataset to perform instance-based learning and classification. A comparison to kernel methods used in Support Vector Machines shows that our approach is superior in terms of classification accuracy. Moreover, we show how our approach can be used for Semantic Web service classification and automatic semantic annotation.http://www.bibsonomy.org/bibtex/2f1fe0cff91ed80024b71584fea8e0a89/dblpdblp2008-05-27T00:00:00+02:00dblp <span class="authorEditorList"><a href="/author/Kiefer">Christoph Kiefer</a>, <a href="/author/Bernstein">Abraham Bernstein</a>, and <a href="/author/Locher">André Locher</a> </span><em>ESWC, </em><em>volume 5021 of Lecture Notes in Computer Science, </em><em>page 478-492. </em><em>Springer, </em>(<em>2008</em>)Tue May 27 00:00:00 CEST 2008ESWCconf/esws/2008478-492Lecture Notes in Computer ScienceAdding Data Mining Support to SPARQL Via Statistical Relational Learning Methods.50212008dblp dblp