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An explosion in the use of RDF for representing information about resources has driven the requirements for Web-scale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and(More)
The advent of Linked Open Data has seen a large number of structured datasets from various domains made available to the public. These datasets are seen as a key enabler for the Semantic Web, where applications can consume and combine this data in powerful and meaningful ways. However, the uptake of Linked Data during this 'introductory phase' is hampered(More)
LDSR is a collection of datasets from the Linked Open Data (LOD) W3C community project, which have been selected and refined for the purpose of presenting a useful perspective to some of the central LOD datasets and to present a good use-case for large-scale reasoning and data integration. The design objectives are as follows: (i) consistency with respect(More)
Semantic repositories – RDF databases with inferencer and query answering engine – are set to become a cornerstone of the Semantic Web (and Linked Open Data) due to their ability to store and reason with the massive quantities of data involved. In this paper, we describe the features of BigOWLIM that have allowed it to penetrate into the commercial sector,(More)
In this paper we present the reasoning mechanism in the OWLIM family of semantic repositories, which is based on materialization. This mechanism is evaluated using a combination of datasets from the Linked Open Data cloud in a public service called FactForge, where the benefits of materialization are manifested in improved SPARQL query performance. 1(More)
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