RDFSlice: Large-scale RDF Dataset Slicing

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In the last years an increasing number of structured data was 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 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, RDF dataset slicing.

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RDFSlicing focuses on the selection and extraction. It devises a fragment of 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 IRI in its join conditions. This restriction guarantees the efficient processing of the query against a sequential dataset dump stream. As a result 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 and retrieving the slice by executing the query against the triple store's SPARQL endpoint.

Project Team


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Dear all, we are happy to announce the 0.2 release of SML-Bench, our Structured Machine Learning benchmark framework. SML-Bench provides full benchmarking scenarios for inductive supervised machine learning covering different knowledge representation languages like OWL and Prolog. Read more about "SML-Bench 0.2 Released"

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ESWC 2017 accepted two Demo Papers by AKSW members ( 2017-04-19T10:19:43+02:00 Christopher Schulz)

2017-04-19T10:19:43+02:00 Christopher Schulz

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