|
Ontology based Data Integration involves the use of ontology(s) to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process. ==Background== Data from multiple sources are characterized by multiple types of heterogeneity. The following hierarchy is often used:〔(AHM02 Tutorial 5: Data Integration and Mediation; Contributors: B. Ludaescher, I. Altintas, A. Gupta, M. Martone, R. Marciano, X. Qian )〕 * Syntactic Heterogeneity: is a result of differences in representation format of data * Schematic or Structural Heterogeneity: the native model or structure to store data differ in data sources leading to structural heterogeneity. Schematic heterogeneity that particularly appears in structured databases is also an aspect of structural heterogeneity.〔 * Semantic Heterogeneity: differences in interpretation of the 'meaning' of data are source of semantic heterogeneity * System Heterogeneity: use of different operating system, hardware platforms lead to system heterogeneity Ontologies, as formal models of representation with explicitly defined concepts and named relationships linking them, are used to address the issue of semantic heterogeneity in data sources. In domains like bioinformatics and biomedicine, the rapid development, adoption and public availability of ontologies () has made it possible for the data integration community to leverage them for semantic integration of data and information. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Ontology-based data integration」の詳細全文を読む スポンサード リンク
|