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A unified approach for aligning taxonomies and debugging taxonomies and their alignments
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology. (IDA/ADIT)ORCID iD: 0000-0002-9084-0470
2013 (English)In: The Semantic Web: Semantics and Big Data: 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings / [ed] Philipp Cimiano, Oscar Corcho, Valentina Presutti, Laura Hollink, Sebastian Rudolph, Springer Berlin/Heidelberg, 2013, 1-15 p.Chapter in book (Refereed)
Abstract [en]

With the increased use of ontologies in semantically-enabled applications, the issues of debugging and aligning ontologies have become increasingly important. The quality of the results of such applications is directly dependent on the quality of the ontologies and mappings between the ontologies they employ. A key step towards achieving high quality ontologies and mappings is discovering and resolving modeling defects, e.g., wrong or missing relations and mappings. In this paper we present a unified framework for aligning taxonomies, the most used kind of ontologies, and debugging taxonomies and their alignments, where ontology alignment is treated as a special kind of debugging. Our framework supports the detection and repairing of missing and wrong is-a structure in taxonomies, as well as the detection and repairing of missing (alignment) and wrong mappings between ontologies. Further, we implemented a system based on this framework and demonstrate its benefits through experiments with ontologies from the Ontology Alignment Evaluation Initiative.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. 1-15 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 7882
Keyword [en]
Computer science, Database management, Information storage and retrieval systems, Artificial intelligence, Database Management, Database Management, Computer Science, general
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-93643DOI: 10.1007/978-3-642-38288-8_1ISBN: 978-3-642-38287-1 (print)ISBN: 978-3-642-38288-8 (print)OAI: oai:DiVA.org:liu-93643DiVA: diva2:625352
Funder
Swedish e‐Science Research CenterSwedish Research Council, 2010-4759CUGS (National Graduate School in Computer Science)
Available from: 2013-06-04 Created: 2013-06-04 Last updated: 2017-11-16Bibliographically approved
In thesis
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