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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.

Over the last years, a considerable amount of structured data has been published on the Web as Linked Open Data (LOD). Despite recent advances, consuming and using Linked Open Data within an organization is still a substantial challenge. Many of the LOD datasets are quite large and despite progress in Resource Description Framework (RDF) data management their loading and querying within a triple store is extremely time-consuming and resource-demanding. To overcome this consumption obstacle, we propose a process inspired by the classical Extract-Transform-Load (ETL) paradigm. In this article, we focus particularly on the selection and extraction steps of this process. We devise a fragment of SPARQL Protocol and RDF Query Language (SPARQL) dubbed SliceSPARQL, which enables the selection of well-defined slices of datasets fulfilling typical information needs. SliceSPARQL supports graph patterns for which each connected subgraph pattern involves a maximum of one variable or Internationalized resource identifier (IRI) in its join conditions. This restriction guarantees the efficient processing of the query against a sequential dataset dump stream. Furthermore, we evaluate our slicing approach on three different optimization strategies. Results show that dataset slices can be generated an order of magnitude faster than by using the conventional approach of loading the whole dataset into a triple store.

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