BibSonomyburst/user/eswc2008/miningBibSonomy RSS feed for /user/eswc2008/mining2012-02-16T13:11:51+01:00http://www.bibsonomy.org/bibtex/2dc85f0d776078a6b46419598834afa09/eswc2008eswc20082008-05-28T14:49:54+02:00adaptation applications data internet language mining modeling ontologies rich rule semantic user user-interfaces-and-personalization web <span class="authorEditorList"><a href="/author/Schmidt">Kay-Uwe Schmidt</a>, <a href="/author/Dörflinger">Jörg Dörflinger</a>, <a href="/author/Rahmani">Tirdad Rahmani</a>, <a href="/author/Sahbi">Mehdi Sahbi</a>, <a href="/author/Thomas">Susan Thomas</a>, and <a href="/author/Stojanovic">Ljiljana Stojanovic</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 14:49:54 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSAn User Interface Adaptation Architecture for \\Rich Internet Applications2008adaptation applications data internet language mining modeling ontologies rich rule semantic user user-interfaces-and-personalization web The need for adaptive and personalized Rich Internet Application puts a new dimension to already existing approaches of Adaptive Hypermedia Systems. Instead of computing the adaptation steps at the server, Rich Internet Applications need a client-side approach that can react immediately on user input. In this paper we present a novel approach that holistically combines page annotations, semantic Web usage mining, user modeling, ontologies and rules to adapt AJAX pages. The focus of our pater is the conceptual introduction of the autonomous client. An autonomous client directly executes all necessary adaptation steps based on a user model, without requesting any logic on the server. In order to realize this, we use ontologies to annotate Rich Internet Applications and to describe the user model as well as semantic Web usage mining for detecting adaptation rules. Additionally, we provide a detailed overview and evaluation of how we moved resource-intensive ontology processing and rules execution from the server to the client.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.