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Matteo Busanelli

Help:Using SPARQL and RDF stores - semantic-mediawiki.org - 0 views

  • In SMW 1.6.0, stores are required to accept updates and queries that do not specify a graph but it is planned to remove this limitation in the future.
  • The first line tells SMW to use the SPARQL store implementation to store data (instead of the SMWSQLStore2 that is the default). The remaining lines provide the relevant service locations, where the last line can be omitted if not applicable. By default, SMW will use a generic SPARQL connector that is based on recent SPARQL documents. Some RDF databases might not be fully compatible with this or might need special tweaks to make use of advanced, non-standard features. For this purpose, it is possible to change the SPARQL connector that SMW uses by setting the variable $smwgSparqlDatabase. In SMW 1.6.0, there is only one special connector:
  • $smwgSparqlDatabase = 'SMWSparqlDatabase4Store';
  • ...2 more annotations...
  • Missing experience regarding performance and stability: There are a number of industry-strength RDF databases available today, some of them free/open source. Yet, the experience of using these systems with SMW are still limited, so some testing is needed before deciding on a particular backend for a large-scale SMW application.
  • no named graphs are used in SMW queries
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    By default, SMW stores all data in the MySQL database that is used by MediaWiki. This ensures a simple setup but it is not an ideal solution for the data format and data access methods that SMW needs. A more natural data model for SMW data is RDF, a data format that organizes information in graphs rather than in fixed database tables. It is possible to use such systems in addition to the SQL database for managing SMW data and for answering queries. This page explains the details
Matteo Busanelli

semanticweb.com - The Voice of Semantic Web Business - 0 views

  • The new CSV on the Web Working Group is an important step in that direction, following on the heels of efforts such as R2RML. It’s about providing metadata about CSV files, such as column headings, data types, and annotations, and, with it, making it easily possible to convert CSV into RDF (or other formats), easing data integration
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