Title: Semantics-aware matching strategy (SAMS) for the Ontology meDiated Data Integration (ODDI)

Authors: Marcello Leida, Paolo Ceravolo, Ernesto Damiani, Zhan Cui, Alex Gusmini

Addresses: EBTIC, Khalifa University, Abu Dhabi Campus, P.O. Box 127788, Abu Dhabi, UAE. ' Dipartimento di Tecnologie dell'Informazione, Universita degli studi di Milano, via Bramante, 65 26013 Crema (CR), Italy. ' Dipartimento di Tecnologie dell'Informazione, Universita degli studi di Milano, via Bramante, 65 26013 Crema (CR), Italy. ' BT Innovate, BT Group, Orion Building, Adastral Park, Martlesham Heath, Ipswich, Suffolk IP5 3RE, UK. ' BT Innovate, BT Group, Orion Building, Adastral Park, Martlesham Heath, Ipswich, Suffolk IP5 3RE, UK

Abstract: Data integration systems are used to integrate heterogeneous data sources in a single view. Recent work on business intelligence highlights the need of on-time, reliable and sound data access systems relying on methods based on semi-automatic procedures. A crucial factor for any semi-automatic algorithm is that of the matching strategy. Different categories of matching operators carry different semantics. For this reason, combining them into a single strategy is a non-trivial process that has to take into account a variety of options. This paper presents SAMS, a matching strategy based on a semantics-aware categorisation of matching operators that allows to group similar attributes on a semantically-rich form.

Keywords: business intelligence; data integration; automatic mapping generation; matching strategy; ontology matching; ontologies; semantics; matching operators.

DOI: 10.1504/IJKESDP.2010.030465

International Journal of Knowledge Engineering and Soft Data Paradigms, 2010 Vol.2 No.1, pp.33 - 56

Available online: 17 Dec 2009 *

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