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The Linked Data cloud has grown to become the largest knowledge base ever constructed. Its size is now turning into a major bottleneck for many applications. In order to facilitate access to this structured information, this paper proposes an automatic sampling method targeted at maximizing answer coverage for applications using SPARQL querying. The approach presented in this paper is novel: no similar RDF sampling approach exist. Additionally, the concept of creating a sample aimed at maximizing SPARQL answer coverage, is unique. We empirically show that the relevance of triples for sampling (a semantic notion) is influenced by the topology of the graph (purely structural), and can be determined without prior knowledge of the queries. Experiments show a significantly higher recall of topology based sampling methods over ran- dom and naive baseline approaches (e.g. up to 90% for Open-BioMed at a sample size of 6%).
|Title of host publication||The Semantic Web – ISWC 2014|
|Subtitle of host publication||13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II|
|Editors||Peter Mika, Abraham Bernstein, Chris Welty, Craig Knoblock, Denny Vrandečić, Paul Groth, Natasha Noy, Krzysztof Janowicz, Carole Goble|
|Number of pages||16|
|Publication status||Published - 2014|
|Name||Lecture Notes in Computer Science|
- graph analysis
- Linked Data
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