Abstract
Abstract Schema matching is a fundamental issue to many database applications, such as query mediation and data warehousing. It becomes a challenge when different vocabularies are used to refer to the same real-world concepts. In this context, a convenient approach, sometimes called extensional, instance-based, or semantic, is to detect how the same real world objects are represented in different databases and to use the information thus obtained to match the schemas. Additionally, we argue that automatic approaches of schema matching should store provenance data about matchings. This paper describes an instance-based schema matching technique for an OWL dialect and proposes a data model for storing provenance data. The matching technique is based on similarity functions and is backed up by experimental results with real data downloaded from data sources found on the Web.
Highlights
A database conceptual schema, or a schema, is a high level description of how database concepts are organized
We proposed hybrid matching techniques based on instance values and on schema information, such as datatypes, cardinality, and relationships
The techniques essentially differ on the nature of the sets to be compared and on the similarity functions adopted
Summary
A database conceptual schema, or a schema, is a high level description of how database concepts are organized. The problem of finding schema matchings becomes a challenge when different vocabularies are used to refer to the same real-world concepts [7]. In this case, a convenient approach, sometimes called extensional, instance-based or semantic, is to detect how the same real-world objects are represented in different databases and to use the information obtained to match the schemas. Unlike any of the above techniques, the schema matching process we propose uses similarity functions to induce vocabulary matchings in a non-trivial way, using an expressive OWL dialect.
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