Article

LOTUS: Adaptive Text Search for Big Linked Data

Authors:

Filip Ilievski,

Laurens Rietveld,

Stefan SchlobachAuthors Info & Claims

Proceedings of the 13th International Conference on The Semantic Web. Latest Advances and New Domains - Volume 9678

Pages 470 - 485

https://doi.org/10.1007/978-3-319-34129-3_29

Published: 29 May 2016 Publication History

Abstract

Finding relevant resources on the Semantic Web today is a dirty job: no centralized query service exists and the support for natural language access is limited. We present LOTUS: Linked Open Text UnleaShed, a text-based entry point to a massive subset of today's Linked Open Data Cloud. Recognizing the use case dependency of resource retrieval, LOTUS provides an adaptive framework in which a set of matching and ranking algorithms are made available. Researchers and developers are able to tune their own LOTUS index by choosing and combining the matching and ranking algorithms that suit their use case best. In this paper, we explain the LOTUS approach, its implementation and the functionality it provides. We demonstrate the ease with which LOTUS enables text-based resource retrieval at an unprecedented scale in concrete and domain-specific scenarios. Finally, we provide evidence for the scalability of LOTUS with respect to the LOD Laundromat, the largest collection of easily accessible Linked Open Data currently available.

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Cited By

Mountantonakis MTzitzikas Y(2020)Content-based Union and Complement Metrics for Dataset Search over RDF Knowledge GraphsJournal of Data and Information Quality10.1145/337275012:2(1-31)Online publication date: 24-Apr-2020
https://dl.acm.org/doi/10.1145/3372750
Kadilierakis GNikas CFafalios PPapadakos PTzitzikas Y(2020)Elas4RDF: Multi-perspective Triple-Centered Keyword Search over RDF Using ElasticsearchThe Semantic Web: ESWC 2020 Satellite Events10.1007/978-3-030-62327-2_21(122-128)Online publication date: 31-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-62327-2_21
Mountantonakis MTzitzikas Y(2019)Large-scale Semantic Integration of Linked DataACM Computing Surveys10.1145/334555152:5(1-40)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3345551
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Information & Contributors

Information

Published In

cover image Guide Proceedings

Proceedings of the 13th International Conference on The Semantic Web. Latest Advances and New Domains - Volume 9678

May 2016

863 pages

ISBN:9783319341286

Editors:
Harald Sack
Universität Potsdam, Potsdam, Germany
,
Eva Blomqvist
Linköping University, Linköping, Sweden
,
Mathieu D'Aquin
The Open University, Milton Keynes, United Kingdom
,
Chiara Ghidini
Fondazione Bruno Kessler, Trento, Italy
,
Simone Paolo Ponzetto
Universität Mannheim, Mannheim, Germany
,
Christoph Lange
Universität Bonn, Bonn, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 29 May 2016

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Cited By

Mountantonakis MTzitzikas Y(2020)Content-based Union and Complement Metrics for Dataset Search over RDF Knowledge GraphsJournal of Data and Information Quality10.1145/337275012:2(1-31)Online publication date: 24-Apr-2020
https://dl.acm.org/doi/10.1145/3372750
Kadilierakis GNikas CFafalios PPapadakos PTzitzikas Y(2020)Elas4RDF: Multi-perspective Triple-Centered Keyword Search over RDF Using ElasticsearchThe Semantic Web: ESWC 2020 Satellite Events10.1007/978-3-030-62327-2_21(122-128)Online publication date: 31-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-62327-2_21
Mountantonakis MTzitzikas Y(2019)Large-scale Semantic Integration of Linked DataACM Computing Surveys10.1145/334555152:5(1-40)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3345551
Beek WIlievski FDebattista JSchlobach SWielemaker JZaveri AKontokostas DHellmann SUmbrich J(2018)Literally betterSemantic Web10.3233/SW-1702889:1(131-150)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/SW-170288
Mountantonakis MTzitzikas Y(2018)Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked DatasetsJournal of Data and Information Quality10.1145/31657139:3(1-49)Online publication date: 27-Jan-2018
https://dl.acm.org/doi/10.1145/3165713

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