A Semi-Automatic Approach For Global-Schema Construction in Data Integration Systems

Author:

Niang C.1,Markhoff B. Bouchou2,Sam Y.2,Lo M.3

Affiliation:

1. Laboratoire d’Informatique (LI), Université François Rabelais Tours, Blois, France, & Laboratoire d’Analyse Numérique et d’Informatique (LANI), Université Gaston Berger, Saint-Louis, Senegal

2. Laboratoire d’Informatique (LI), Université François Rabelais Tours, Blois, France

3. Laboratoire d’Analyse Numérique et d’Informatique (LANI), Université Gaston Berger, Saint-Louis, Senegal

Abstract

Data integration involves combining data residing in different sources and providing users with a unified view of these data through what is called a “global schema’’. The authors address here the problem of the construction of this global schema, with a minimum human effort, in the semantic Web context where data sources are annotated with ontologies. The authors aim to facilitate the task of building a common vocabulary (ontology) that will serve as a shared conceptual level for several heterogeneous data sources needing to share their data in a specific application domain. The authors propose a solution based on the use of a domain reference ontology (or “background knowledge’’) as a mediation support and some reasoning techniques in order to minimize human intervention. The work presented here is implemented as a prototype for Semi Automatic Global Ontology Building (SAGOB).

Publisher

IGI Global

Subject

General Engineering

Reference35 articles.

1. Aleksovski, Z., Klein, M. C. A., Kate, W. ten, & Harmelen, F. van (2006a). Matching unstructured vocabularies using a background ontology. EKAW, 182–197.

2. Aleksovski, Z., Klein, M. C. A., ten Kate, W., & Van Harmelen, F. (2007). Using multiple ontologies as background knowledge in ontology matching. In S. S. A. Y. Avrithis, Y. Kompatsiasris, (Ed.), In Proceedings of the 1st International Workshop on Collective Semantics: Collective Intelligence & the Semantic Web (CISeb’08).

3. Computing the least common subsumer w.r.t. a background terminology

4. Baeza, Y. R. A., & Ribeiro, N. B. A (1999). Modern information retrieval. ACM Press / Addisson-Wesley.

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