Detecting inconsistency in biological molecular databases using ontologies

Chen, Qingfeng, Chen, Yi-Ping Phoebe and Zhang, Chengqi 2007, Detecting inconsistency in biological molecular databases using ontologies, Data mining and knowledge discovery, vol. 15, no. 2, pp. 275-296, doi: 10.1007/s10618-007-0071-0.

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Title Detecting inconsistency in biological molecular databases using ontologies
Author(s) Chen, Qingfeng
Chen, Yi-Ping Phoebe
Zhang, Chengqi
Journal name Data mining and knowledge discovery
Volume number 15
Issue number 2
Start page 275
End page 296
Publisher Kluwer Academic Publishers
Place of publication Boston, Mass.
Publication date 2007-10
ISSN 1384-5810
Keyword(s) data preparation
biological molecular databases
Summary The rapid growth of life science databases demands the fusion of knowledge from heterogeneous databases to answer complex biological questions. The discrepancies in nomenclature, various schemas and incompatible formats of biological databases, however, result in a significant lack of interoperability among databases. Therefore, data preparation is a key prerequisite for biological database mining. Integrating diverse biological molecular databases is an essential action to cope with the heterogeneity of biological databases and guarantee efficient data mining. However, the inconsistency in biological databases is a key issue for data integration. This paper proposes a framework to detect the inconsistency in biological databases using ontologies. A numeric estimate is provided to measure the inconsistency and identify those biological databases that are appropriate for further mining applications. This aids in enhancing the quality of databases and guaranteeing accurate and efficient mining of biological databases.
Language eng
DOI 10.1007/s10618-007-0071-0
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Springer
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Document type: Journal Article
Collection: School of Engineering and Information Technology
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