Abstract. Schema matching is a fundamental issue to many database applications, such as query me-diation 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 ...
Schema matching is the process of developing semantic matches between two or more schemas. The purpo...
In recent years, the growing availability of open-accessed data ( Wikipedia) combined with the advan...
Schema matching is a critical problem for integrating heterogeneous information sources. Traditional...
Abstract: This paper describes a software tool that implements an instance-based schema matching tec...
In a large and decentralised knowledge representation system such as the Web of Data, it is common f...
Schema matching is a crucial phase in data integration that aims to find correspondences between sch...
The main issue concern of schema matching is how to support the merging decision by providing matchi...
Existing techniques for schema matching are classified as either schema-based, instance-based, or a ...
Instance based matching is the process of comparing data from different heterogeneous data sources i...
Schema matching is considered as one of the essential phases of data integration in database systems...
AbstractInstance based matching is the process of comparing data from different heterogeneous data s...
Instance based schema matching is the process of comparing instances from different heterogeneous da...
This dissertation studies the schema matching problem that finds semantic correspondences (called ma...
Abstract. Instance based ontology matching is a new approach which uses the extensions of concepts t...
Schema matching is a basic problem in many database application domains, such as data integration, E...
Schema matching is the process of developing semantic matches between two or more schemas. The purpo...
In recent years, the growing availability of open-accessed data ( Wikipedia) combined with the advan...
Schema matching is a critical problem for integrating heterogeneous information sources. Traditional...
Abstract: This paper describes a software tool that implements an instance-based schema matching tec...
In a large and decentralised knowledge representation system such as the Web of Data, it is common f...
Schema matching is a crucial phase in data integration that aims to find correspondences between sch...
The main issue concern of schema matching is how to support the merging decision by providing matchi...
Existing techniques for schema matching are classified as either schema-based, instance-based, or a ...
Instance based matching is the process of comparing data from different heterogeneous data sources i...
Schema matching is considered as one of the essential phases of data integration in database systems...
AbstractInstance based matching is the process of comparing data from different heterogeneous data s...
Instance based schema matching is the process of comparing instances from different heterogeneous da...
This dissertation studies the schema matching problem that finds semantic correspondences (called ma...
Abstract. Instance based ontology matching is a new approach which uses the extensions of concepts t...
Schema matching is a basic problem in many database application domains, such as data integration, E...
Schema matching is the process of developing semantic matches between two or more schemas. The purpo...
In recent years, the growing availability of open-accessed data ( Wikipedia) combined with the advan...
Schema matching is a critical problem for integrating heterogeneous information sources. Traditional...