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Ontology Mapping by Axioms (OMA)

Ontology Mapping by Axioms (OMA)

Published: 2005 April
Herausgeber: Klaus-Dieter Althoff, Andreas Dengel, Ralph Bergmann, Markus Nick, Thomas Roth-Berghofer
Buchtitel: Professional Knowledge Management: Third Biennial Conference, WM 2005, Kaiserslautern, Germany, April 10-13, 2005, Revised Selected Papers
Ausgabe: 3782
Reihe: LNCS
Seiten: 560 - 569
Verlag: Springer

Referierte Veröffentlichung


Mapping of ontologies generalizes a number of problems occurring in realistic semantic web applications such as the following ones: (I) Data integration is concerned with the use of data from different sources in one application. The data from the different sources needs to be presented to the user in a unified way. Typical scenarios include (a) the integration of relational databases into semantic web applications, where database schemas are first “lifted” to the ontology level and then mapped to the ontologies of the applications, and (b) the usage of different heterogeneous ontologies within semantic web applications (e.g. by reusing different domain ontologies). (II) Ontology evolution deals with the fact that in reality ontologies are typically not static entities but they rather evolve over time. This holds also for ontologies which participate in mappings to other ontologies. Therefore one can further distinguish between (a) evolving ontology mappings, (b) mapping different versions of multiple evolving ontology, and (c) mapping between versions of one ontology. To handle the increasing number of individual ontologies, but also for being able to create mappings on the fly, it becomes necessary to develop automatic approaches. In this paper, we determine mappings based on the similarity of the features of individual ontological entities. We show that mappings can be derived automatically and on the fly by encoding similarities into logical axioms and processing them with an inference engine. The advantages of this approach are obvious. Firstly, the axioms can easily be reused for mappings of arbitrary ontologies, no additional modeling effort is required. Secondly, the inference engine is the only mandatory technological infrastructure which means that no additional implementation effort is needed.

ISBN: 3-540-30465-7
ISSN: 0302-9743
VG Wort-Seiten: 16
Download: Media:2005_881_Ehrig_Ontology_Mappin_1.pdf
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Semantic Web, Ontologiebasierte Wissensmanagementsysteme