LOD lab: Experiments at LOD scale

Laurens Rietveld, Wouter Beek, Stefan Schlobach

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review


Contemporary Semantic Web research is in the business of optimizing algorithms for only a handful of datasets such as DBpedia, BSBM, DBLP and only a few more. This means that current practice does not generally take the true variety of Linked Data into account. With hundreds of thousands of datasets out in the world today the re- sults of SemanticWeb evaluations are less generalizable than they should and—this paper argues—can be. This paper describes LOD Lab: a fun- damentally different evaluation paradigm that makes algorithmic evalu- ation against hundreds of thousands of datasets the new norm. LOD Lab is implemented in terms of the existing LOD Laundromat architecture combined with the new open-source programming interface Frank that supportsWeb-scale evaluations to be run from the command-line.We il- lustrate the viability of the LOD Lab approach by rerunning experiments from three recent SemanticWeb research publications and expect it will contribute to improving the quality and reproducibility of experimental work in the Semantic Web community. We show that simply rerunning existing experiments within this new evaluation paradigm brings up in- teresting research questions as to how algorithmic performance relates to (structural) properties of the data. 1
Original languageEnglish
Title of host publicationThe Semantic Web-ISWC 2015
Subtitle of host publication14th International Semantic Web Conference Bethlehem, PA, USA, October 11–15, 2015, Proceedings
Number of pages17
VolumePart II
ISBN (Electronic)9783319250106
ISBN (Print)9783319250090
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


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