Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling

Laurens Rietveld; Rinke Hoekstra; Stefan Schlobach; Christophe Guéret

doi:10.1007/978-3-319-11915-1_6

Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling

Laurens Rietveld, Rinke Hoekstra, Stefan Schlobach, Christophe Guéret

DANS-KNAW

Research output: Chapter in book/volume › Contribution to conference proceedings › Scientific › peer-review

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Abstract

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%).

Original language	English
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
Publisher	Springer
Pages	81-96
Number of pages	16
Volume	8797
ISBN (Electronic)	978-3-319-11915-1
ISBN (Print)	978-3-319-11914-4
DOIs	https://doi.org/10.1007/978-3-319-11915-1_6
Publication status	Published - 2014

Publication series

Name	Lecture Notes in Computer Science
ISSN (Print)	0302-9743

Keywords

subgraphs
sampling
graph analysis
ranking
Linked Data

Access to Document

10.1007/978-3-319-11915-1_6

Structural_Properties_as_Proxy_for_Semantic_Relevance_in_RDF_Graph_SamplingSubmitted manuscript, 428 KB

http://link.springer.com/chapter/10.1007%2F978-3-319-11915-1_6

Cite this

Rietveld, L., Hoekstra, R., Schlobach, S., & Guéret, C. (2014). Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling. In P. Mika, A. Bernstein, C. Welty, C. Knoblock, D. Vrandečić, P. Groth, N. Noy, K. Janowicz, & C. Goble (Eds.), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II (Vol. 8797, pp. 81-96). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-11915-1_6

Rietveld, Laurens ; Hoekstra, Rinke ; Schlobach, Stefan ; Guéret, Christophe. / Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling. The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II. editor / Peter Mika ; Abraham Bernstein ; Chris Welty ; Craig Knoblock ; Denny Vrandečić ; Paul Groth ; Natasha Noy ; Krzysztof Janowicz ; Carole Goble. Vol. 8797 Springer, 2014. pp. 81-96 (Lecture Notes in Computer Science).

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abstract = "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%).",

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Rietveld, L, Hoekstra, R, Schlobach, S & Guéret, C 2014, Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling. in P Mika, A Bernstein, C Welty, C Knoblock, D Vrandečić, P Groth, N Noy, K Janowicz & C Goble (eds), The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II. vol. 8797, Lecture Notes in Computer Science, Springer, pp. 81-96. https://doi.org/10.1007/978-3-319-11915-1_6

Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling. / Rietveld, Laurens; Hoekstra, Rinke; Schlobach, Stefan; Guéret, Christophe.

The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II. ed. / Peter Mika; Abraham Bernstein; Chris Welty; Craig Knoblock; Denny Vrandečić; Paul Groth; Natasha Noy; Krzysztof Janowicz; Carole Goble. Vol. 8797 Springer, 2014. p. 81-96 (Lecture Notes in Computer Science).

Research output: Chapter in book/volume › Contribution to conference proceedings › Scientific › peer-review

TY - CHAP

T1 - Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling

AU - Rietveld, Laurens

AU - Hoekstra, Rinke

AU - Schlobach, Stefan

AU - Guéret, Christophe

PY - 2014

Y1 - 2014

N2 - 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%).

AB - 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%).

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KW - sampling

KW - graph analysis

KW - ranking

KW - Linked Data

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A2 - Knoblock, Craig

A2 - Vrandečić, Denny

A2 - Groth, Paul

A2 - Noy, Natasha

A2 - Janowicz, Krzysztof

A2 - Goble, Carole

PB - Springer

ER -

Rietveld L, Hoekstra R, Schlobach S, Guéret C. Structural Properties as Proxy for Semantic Relevance in RDF Graph Sampling. In Mika P, Bernstein A, Welty C, Knoblock C, Vrandečić D, Groth P, Noy N, Janowicz K, Goble C, editors, The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II. Vol. 8797. Springer. 2014. p. 81-96. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-11915-1_6