Scalable Semantic Version Control for Linked Data Management
published: July 15, 2015, recorded: June 2015, views: 1402
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Linked Data is the Semantic Web's established standard for publishing and interlinking data. When authors collaborate on a data set, distribution and tracking of changes are crucial aspects. Several approaches for version control were presented in this area each focusing on different aspects and bounded to different limitations. In this paper we present an approach for semantic version control for dynamic Linked Data based on a delta-based storage strategy and on-demand reconstruction of historic versions. The approach is intended for large data sets and supports targeted and cross-version queries. The approach was implemented prototypically on top of a scalable triple store and tested with a generated data set based on parts of DBpedia with several million triples and thousands of versions.
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