Title :
Robust Module-Based Data Management
Author :
Goasdoue, Francois ; Rousset, Marie-Christine
Author_Institution :
INRIA Saclay & Lab. de Rech. en Inf. (LRI), Univ. Paris-Sud, Orsay, France
Abstract :
The current trend for building an ontology-based data management system (DMS) is to capitalize on efforts made to design a preexisting well-established DMS (a reference system). The method amounts to extracting from the reference DMS a piece of schema relevant to the new application needs-a module-, possibly personalizing it with extra constraints w.r.t. the application under construction, and then managing a data set using the resulting schema. In this paper, we extend the existing definitions of modules and we introduce novel properties of robustness that provide means for checking easily that a robust module-based DMS evolves safely w.r.t. both the schema and the data of the reference DMS. We carry out our investigations in the setting of description logics which underlie modern ontology languages, like RDFS, OWL, and OWL2 from W3C. Notably, we focus on the DL-liteA dialect of the DL-lite family, which encompasses the foundations of the QL profile of OWL2 (i.e., DL-liteR): the W3C recommendation for efficiently managing large data sets.
Keywords :
data handling; ontologies (artificial intelligence); W3C recommendation; description logics; ontology based data management system; ontology language; reference system; robust module based DMS; robust module based data management; Artificial intelligence; Data models; Knowledge management; Resource description framework; Semantic Web; Models and principles; Semantic Web; algorithms for data and knowledge management; artificial intelligence; database management; intelligent web services; personalization;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2011.255