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<bibtex:entry id="blanco-fernandez2008semantic">
  <bibtex:inproceedings>
    <bibtex:author>Blanco&#45;Fernandez&#44; Yolanda and Pazos&#45;Arias&#44; Jos&#233; J. and Gil&#45;Solla&#44; Alberto and Ramos&#45;Cabrer&#44; Manuel and Lopez&#45;Nores&#44; Martin</bibtex:author>
    <bibtex:editor>Hauswirth&#44; Manfred and Koubarakis&#44; Manolis and Bechhofer&#44; Sean</bibtex:editor>
    <bibtex:title>Semantic Reasoning: A Path To New Possibilities of Personalization</bibtex:title>
    <bibtex:booktitle>Proceedings of the 5th European Semantic Web Conference</bibtex:booktitle>

    <bibtex:publisher>Springer Verlag</bibtex:publisher>
    <bibtex:year>2008</bibtex:year>

    <bibtex:month>June</bibtex:month>




    <bibtex:series>LNCS</bibtex:series>



    <bibtex:address>Berlin&#44; Heidelberg</bibtex:address>

    <bibtex:abstract>Recommender systems face up to current information overload by selecting automatically items that match the personal preferences of each user.  The  so&#45;called content&#45;based recommenders  suggest items similar to those the user liked in the past&#44; by resorting to syntactic matching  mechanisms. The rigid nature of such mechanisms leads to recommend only items that bear a strong resemblance to those the user already knows. In this paper&#44; we propose a novel content&#45;based strategy that diversifies the offered recommendations by employing reasoning mechanisms borrowed from the Semantic Web. These mechanisms discover extra knowledge about the user&#39;s preferences&#44; thus favoring more accurate and flexible personalization processes. Our approach is generic enough to be used in a wide variety of personalization applications and services&#44; in diverse domains and recommender systems. The proposed reasoning&#45;based strategy has been empirically evaluated with a set of real users. The obtained results evidence computational feasibility and significant increases of recommendation accuracy in relation to existing approaches where our reasoning capabilities are disregarded.</bibtex:abstract>
    <bibtex:url>http://data.semanticweb.org/conference/eswc/2008/papers/11</bibtex:url>






    <bibtex:keywords>techniques activation associations web personalization semantic reasoning spreading user&#45;interfaces&#45;and&#45;personalization</bibtex:keywords>



  </bibtex:inproceedings>
</bibtex:entry>
<bibtex:entry id="castano2008mapping">
  <bibtex:inproceedings>
    <bibtex:author>Castano&#44; Silvana and Ferrara&#44; Alfio and Lorusso&#44; Davide and N&#228;th&#44; Tobias Henrik and Moeller&#44; Ralf</bibtex:author>
    <bibtex:editor>Hauswirth&#44; Manfred and Koubarakis&#44; Manolis and Bechhofer&#44; Sean</bibtex:editor>
    <bibtex:title>Mapping Validation by Probabilistic Reasoning</bibtex:title>
    <bibtex:booktitle>Proceedings of the 5th European Semantic Web Conference</bibtex:booktitle>

    <bibtex:publisher>Springer Verlag</bibtex:publisher>
    <bibtex:year>2008</bibtex:year>

    <bibtex:month>June</bibtex:month>




    <bibtex:series>LNCS</bibtex:series>



    <bibtex:address>Berlin&#44; Heidelberg</bibtex:address>

    <bibtex:abstract>In the semantic web environment&#44; where two or more independent ontologies can be used in order to describe knowledge and data&#44; ontologies have to be aligned by defining mappings among the elements of one ontology and the elements of another ontology. Very often&#44; mappings are not derived by the semantics of the ontologies that are compared&#44; but&#44; rather&#44; by an evaluation of the similarity of the terminology used in the two ontologies or of their syntactic structure. Moreover&#44; ontology mappings can be inaccurate&#44; because ontology matching tools derive such mappings from inaccurate terminology or even because they are not specifically tailored for the domain at hand. In this paper&#44; we propose a new mapping validation approach for interpreting similarity&#45;based mappings as semantic relations&#44; by coping also with inaccuracy situations. The idea is to see two independent ontologies as a unique distributed knowledge base and to assume a semantic interpretation of ontology mappings as probabilistic and hypothetical relations among ontology elements. We present and use a probabilistic reasoning tool in order to validate mappings and to possibly infer new relations among the ontologies.</bibtex:abstract>
    <bibtex:url>http://data.semanticweb.org/conference/eswc/2008/papers/90</bibtex:url>






    <bibtex:keywords>matching mapping probabilistic logics description ontology reasoning formal&#45;languages&#45;1</bibtex:keywords>



  </bibtex:inproceedings>
</bibtex:entry>
<bibtex:entry id="rosati2008finite">
  <bibtex:inproceedings>
    <bibtex:author>Rosati&#44; Riccardo</bibtex:author>
    <bibtex:editor>Hauswirth&#44; Manfred and Koubarakis&#44; Manolis and Bechhofer&#44; Sean</bibtex:editor>
    <bibtex:title>Finite model reasoning in DL&#45;Lite</bibtex:title>
    <bibtex:booktitle>Proceedings of the 5th European Semantic Web Conference</bibtex:booktitle>

    <bibtex:publisher>Springer Verlag</bibtex:publisher>
    <bibtex:year>2008</bibtex:year>

    <bibtex:month>June</bibtex:month>




    <bibtex:series>LNCS</bibtex:series>



    <bibtex:address>Berlin&#44; Heidelberg</bibtex:address>

    <bibtex:abstract>The semantics of OWL&#45;DL and its subclasses are based on the classical semantics of first&#45;order logic&#44; in which the interpretation domain may be an infinite set. This constitutes a serious expressive limitation for such ontology languages&#44; since&#44; in many real application scenarios for the Semantic Web&#44; the domain of interest is actually finite&#44; although the exact cardinality of the domain is unknown. Hence&#44; in these cases the formal semantics of the OWL&#45;DL ontology does not coincide with its intended semantics.  In this paper we start filling this gap&#44; by considering the subclasses of OWL&#45;DL which correspond to the logics of the DL&#45;Lite family&#44; and studying reasoning over finite models in such logics.  In particular&#44; we mainly consider two reasoning problems: deciding satisfiability of an ontology&#44; and answering unions of conjunctive queries (UCQs) over an ontology. We first consider the description logic DL&#45;Lite&#95;R and show that&#44; for the two above mentioned problems&#44; finite model reasoning coincides with classical reasoning&#44; i.e.&#44; reasoning over arbitrary&#44; unrestricted models.  Then&#44; we analyze the description logics DL&#45;Lite&#95;F and DL&#95;Lite&#95;A.  Differently from DL&#45;Lite&#95;R&#44; in such logics finite model reasoning does not coincide with classical reasoning. To solve satisfiability and query answering over finite models in these logics&#44; we define techniques which reduce polynomially both the above reasoning problems over finite models to the corresponding problem over arbitrary models. Thus&#44; for all the DL&#45;Lite languages considered&#44; the good computational properties of satisfiability and query answering under the classical semantics also hold under the finite model semantics.  Moreover&#44; we have effectively and easily implemented the above techniques&#44; extending the DL&#45;Lite reasoner QuOnto with support for finite model reasoning.</bibtex:abstract>
    <bibtex:url>http://data.semanticweb.org/conference/eswc/2008/papers/291</bibtex:url>






    <bibtex:keywords>ontologies reasoning description computational logics complexity formal&#45;languages&#45;2</bibtex:keywords>



  </bibtex:inproceedings>
</bibtex:entry>
</bibtex:file>

