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  • × theme_ss:"Semantische Interoperabilität"
  • × year_i:[2000 TO 2010}
  1. Binz, V.; Rühle, S.: KIM - Das Kompetenzzentrum Interoperable Metadaten (2009) 0.00
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  2. Wilde, E.: Semantische Interoperabilität von XML Schemas (2005) 0.00
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  3. Krause, J.: Konkretes zur These, die Standardisierung von der Heterogenität her zu denken (2004) 0.00
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  4. Gödert, W.: Multilingualität und Lokalisierung zur Wissenserkundung : oder vom Nutzen semantischer Netze für das Information Retrieval (2007) 0.00
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  5. Staub, P.: Semantische Interoperabilität : Voraussetzung für Geodaten-Infrastrukturen (2009) 0.00
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  6. Mao, M.: Ontology mapping : towards semantic interoperability in distributed and heterogeneous environments (2008) 0.00
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    Abstract
    This dissertation studies ontology mapping: the problem of finding semantic correspondences between similar elements of different ontologies. In the dissertation, elements denote classes or properties of ontologies. The goal of this research is to use ontology mapping to make heterogeneous information more accessible. The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semiautomated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests.
  7. Stempfhuber, M.: Verbesserung der Zugänglichkeit heterogen erschlossener Informationen (2008) 0.00
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  8. Semenova, E.; Stricker, M.: ¬Eine Ontologie der Wissenschaftsdisziplinen : Entwicklung eines Instrumentariums für die Wissenskommunikation (2007) 0.00
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  9. Panzer, M.: Semantische Integration heterogener und unterschiedlichsprachiger Wissensorganisationssysteme : CrissCross und jenseits (2008) 0.00
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  10. Fiala, S.: Deutscher Bibliothekartag Leipzig 2007 : Sacherschließung - Informationsdienstleistung nach Mass (2007) 0.00
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  11. Altenhöner, R; Hengel, C.; Jahns, Y.; Junger, U.; Mahnke, C.; Oehlschläger, S.; Werner, C.: Weltkongress Bibliothek und Information, 74. IFLA-Generalkonferenz in Quebec, Kanada : Aus den Veranstaltungen der Division IV Bibliographic Control, der Core Activities ICADS und UNIMARC sowie der Information Technology Section (2008) 0.00
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