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Automatic Detection of Duplicated Attributes in Ontology

  • Semantic heterogeneity is the ambiguous interpretation of terms describing the meaning of data in heterogeneous data sources such as databases. This is a well-known problem in data integration. A recent solution to this problem is to use ontologies, which is called ontology-based data integration. However, ontologies can contain duplicated attributes, which can lead to improper integration results. This paper proposes a novel approach that analyzes a workload of queries over an ontology to automatically calculate (semantic) distances between attributes, which are then used for duplicate detection.

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Metadaten
Author:Irina Astrova, Arne KoschelORCiDGND
URN:urn:nbn:de:bsz:960-opus4-35295
DOI:https://doi.org/10.25968/opus-3529
DOI original:https://doi.org/10.5220/0001961102830286
ISBN:978-989-8111-84-5
ISSN:2184-4992
Parent Title (English):Proceedings of the 11th International Conference on Enterprise Information
Publisher:SciTePress
Document Type:Conference Proceeding
Language:English
Year of Completion:2009
Publishing Institution:Hochschule Hannover
Release Date:2025/02/14
Tag:Context-based similarity; Duplicated attributes; ICD algorithm; Market basket analysis; Ontology-based data integration
GND Keyword:OntologieGND; Semantische UnbestimmtheitGND; DuplikaterkennungGND; DatenbankGND; DatenqualitätGND; DatenintegrationGND
Page Number:4
First Page:283
Last Page:286
Link to catalogue:1920168958
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:004 Informatik
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International