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

Beek, Wouter and Fernandez Garcia, Javier David and Verborgh, Ruben (2017) LOD-a-lot: A single-file enabler for data science. In: Proceedings of the 13th International Conference on Semantic Systems (Semantics2017). ACM Press, Amsterdam. pp. 181-184.


Download (517kB)


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.

Item Type: Book Section
Additional Information: This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in ACM Digital Library, https://doi.org/10.1145/3132218.3132241. Funded by the research programme MaestroGraph (612.001.552), financed by the Dutch Organization for Scientific Research (NWO), the Austrian Science Fund: M1720-G11, and the European Union's Horizon 2020 research and innovation programme grant 731601.
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft > Polleres
Version of the Document: Accepted for Publication
Variance from Published Version: Minor
Depositing User: Javier David Fernandez Garcia
Date Deposited: 06 Sep 2018 08:44
Last Modified: 06 Sep 2018 13:41
FIDES Link: https://bach.wu.ac.at/d/research/results/84157/
URI: https://epub.wu.ac.at/id/eprint/6492


View Item View Item


Downloads per month over past year

View more statistics