@inproceedings{schmidt2008user,
  abstract = {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.},
  added-at = {2008-05-28T14:49:54.000+0200},
  address = {Berlin, Heidelberg},
  author = {Schmidt, Kay-Uwe and Dörflinger, Jörg and Rahmani, Tirdad and Sahbi, Mehdi and Thomas, Susan and Stojanovic, Ljiljana},
  biburl = {http://www.bibsonomy.org/bibtex/2dc85f0d776078a6b46419598834afa09/eswc2008},
  booktitle = {Proceedings of the 5th European Semantic Web Conference},
  editor = {Hauswirth, Manfred and Koubarakis, Manolis and Bechhofer, Sean},
  interhash = {b2824f43fb8fef23cd8592dfa6ceec41},
  intrahash = {dc85f0d776078a6b46419598834afa09},
  keywords = {adaptation applications data internet language mining modeling ontologies rich rule semantic user user-interfaces-and-personalization web},
  month = {June},
  publisher = {Springer Verlag},
  series = {LNCS},
  timestamp = {2008-05-28T14:49:54.000+0200},
  title = {An User Interface Adaptation Architecture for \\Rich Internet Applications},
  url = {http://data.semanticweb.org/conference/eswc/2008/papers/72},
  year = 2008
}

@inproceedings{kiefer2008adding,
  abstract = {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.},
  added-at = {2008-05-28T14:49:52.000+0200},
  address = {Berlin, Heidelberg},
  author = {Kiefer, Christoph and Bernstein, Abraham and Locher, André},
  biburl = {http://www.bibsonomy.org/bibtex/2569e52ad749e5ea312aae2a76dca52d7/eswc2008},
  booktitle = {Proceedings of the 5th European Semantic Web Conference},
  editor = {Hauswirth, Manfred and Koubarakis, Manolis and Bechhofer, Sean},
  interhash = {c091f27b3e3d70203862e2f3a1132928},
  intrahash = {569e52ad749e5ea312aae2a76dca52d7},
  keywords = {sparql data statistical evaluation mining relational learning query-processing-1},
  month = {June},
  publisher = {Springer Verlag},
  series = {LNCS},
  timestamp = {2008-05-28T14:49:52.000+0200},
  title = {Adding Data Mining Support to SPARQL via Statistical Relational Learning Methods},
  url = {http://data.semanticweb.org/conference/eswc/2008/papers/32},
  year = 2008
}