LOD-a-lot: A single-file enabler for data science

Wouter Beek; Javier David Fernandez Garcia; Ruben Verborgh

doi:10.1145/3132218.3132241

LOD-a-lot: A single-file enabler for data science

Wouter Beek, Javier David Fernandez Garcia, Ruben Verborgh

Vienna University of Economics and Business

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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Abstract

Many data scientists make use of Linked Open Data (LOD) as a huge interconnected knowledge base represented in RDF. However, the distributed nature of the information and the lack of a scalable approach to manage and consume such Big Semantic Data makes it difficult and expensive to conduct large-scale studies. As a consequence, most scientists restrict their analyses to one or two datasets (often DBpedia) that contain at most hundreds of millions of triples.
LOD-a-lot is a dataset that integrates a large portion (over 28 billion triples) of the LOD Cloud into a single ready-to-consume file that can be easily downloaded, shared and queried with a small memory footprint. This paper shows there exists a wide collection of Data Science use cases that can be performed over such a LOD-a-lot file. For these use cases LOD-a-lot significantly reduces the cost and complexity of conducting Data Science.

Original language	English
Title of host publication	International Conference on Semantic Systems
Editors	Rinke Hoekstra and Catherine Faron-Zucker and Tassilo Pellegrini and Victor de Boer
Place of Publication	Amsterdam
Publisher	ACM Press
Pages	181 - 184
DOIs	https://doi.org/10.1145/3132218.3132241
Publication status	Published - 2017

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LOD-a-lot SEMANTiCS2017Accepted author manuscript, 506 KBLicence: Other

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University Library Catalogue

SPECIAL - Scalable Policy-awarE linked data arChitecture for prIvacy, trAnsparency and compLiance
Kirrane, S. (PI - Project head), Drozd, O. (Researcher), Fernandez Garcia, J. D. (Researcher), Havur, G. (Researcher), Polleres, A. (Researcher) & Spiekermann-Hoff, S. (Researcher)
1/01/17 → 31/12/19
Project: Research funding

Cite this

@inproceedings{f6de8c7d17ef4bb58df9f89f0fb6c600,

title = "LOD-a-lot: A single-file enabler for data science",

abstract = "Many data scientists make use of Linked Open Data (LOD) as a huge interconnected knowledge base represented in RDF. However, the distributed nature of the information and the lack of a scalable approach to manage and consume such Big Semantic Data makes it difficult and expensive to conduct large-scale studies. As a consequence, most scientists restrict their analyses to one or two datasets (often DBpedia) that contain at most hundreds of millions of triples. LOD-a-lot is a dataset that integrates a large portion (over 28 billion triples) of the LOD Cloud into a single ready-to-consume file that can be easily downloaded, shared and queried with a small memory footprint. This paper shows there exists a wide collection of Data Science use cases that can be performed over such a LOD-a-lot file. For these use cases LOD-a-lot significantly reduces the cost and complexity of conducting Data Science.",

author = "Wouter Beek and {Fernandez Garcia}, {Javier David} and Ruben Verborgh",

year = "2017",

doi = "10.1145/3132218.3132241",

language = "English",

pages = "181 -- 184",

editor = "{Rinke Hoekstra and Catherine Faron-Zucker and Tassilo Pellegrini and Victor de Boer}",

booktitle = "International Conference on Semantic Systems",

publisher = "ACM Press",

}

LOD-a-lot: A single-file enabler for data science. / Beek, Wouter; Fernandez Garcia, Javier David; Verborgh, Ruben.
International Conference on Semantic Systems. ed. / Rinke Hoekstra and Catherine Faron-Zucker and Tassilo Pellegrini and Victor de Boer. Amsterdam: ACM Press, 2017. p. 181 - 184.

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

TY - GEN

T1 - LOD-a-lot: A single-file enabler for data science

AU - Beek, Wouter

AU - Fernandez Garcia, Javier David

AU - Verborgh, Ruben

PY - 2017

Y1 - 2017

N2 - Many data scientists make use of Linked Open Data (LOD) as a huge interconnected knowledge base represented in RDF. However, the distributed nature of the information and the lack of a scalable approach to manage and consume such Big Semantic Data makes it difficult and expensive to conduct large-scale studies. As a consequence, most scientists restrict their analyses to one or two datasets (often DBpedia) that contain at most hundreds of millions of triples. LOD-a-lot is a dataset that integrates a large portion (over 28 billion triples) of the LOD Cloud into a single ready-to-consume file that can be easily downloaded, shared and queried with a small memory footprint. This paper shows there exists a wide collection of Data Science use cases that can be performed over such a LOD-a-lot file. For these use cases LOD-a-lot significantly reduces the cost and complexity of conducting Data Science.

AB - Many data scientists make use of Linked Open Data (LOD) as a huge interconnected knowledge base represented in RDF. However, the distributed nature of the information and the lack of a scalable approach to manage and consume such Big Semantic Data makes it difficult and expensive to conduct large-scale studies. As a consequence, most scientists restrict their analyses to one or two datasets (often DBpedia) that contain at most hundreds of millions of triples. LOD-a-lot is a dataset that integrates a large portion (over 28 billion triples) of the LOD Cloud into a single ready-to-consume file that can be easily downloaded, shared and queried with a small memory footprint. This paper shows there exists a wide collection of Data Science use cases that can be performed over such a LOD-a-lot file. For these use cases LOD-a-lot significantly reduces the cost and complexity of conducting Data Science.

U2 - 10.1145/3132218.3132241

DO - 10.1145/3132218.3132241

M3 - Contribution to conference proceedings

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EP - 184

BT - International Conference on Semantic Systems

A2 - Rinke Hoekstra and Catherine Faron-Zucker and Tassilo Pellegrini and Victor de Boer, null

PB - ACM Press

CY - Amsterdam

ER -

LOD-a-lot: A single-file enabler for data science

Abstract

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Projects

SPECIAL - Scalable Policy-awarE linked data arChitecture for prIvacy, trAnsparency and compLiance

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