research-article

On measuring the lattice of commonalities among several linked datasets

Authors:

Michalis Mountantonakis,

Yannis TzitzikasAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 9, Issue 12

Pages 1101 - 1112

https://doi.org/10.14778/2994509.2994527

Published: 01 August 2016 Publication History

Abstract

A big number of datasets has been published according to the principles of Linked Data and this number keeps increasing. Although the ultimate objective is linking and integration, it is not currently evident how connected the current LOD cloud is. Measurements (and indexes) that involve more than two datasets are not available although they are important: (a) for obtaining complete information about one particular URI (or set of URIs) with provenance (b) for aiding dataset discovery and selection, (c) for assessing the connectivity between any set of datasets for quality checking and for monitoring their evolution over time, (d) for constructing visualizations that provide more informative overviews. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this paper we introduce indexes (and their construction algorithms) that can speedup such tasks. In brief, we introduce (i) a namespace-based prefix index, (ii) a sameAs catalog for computing the symmetric and transitive closure of the owl:sameAs relationships encountered in the datasets, (iii) a semantics-aware element index (that exploits the aforementioned indexes), and finally (iv) two lattice-based incremental algorithms for speeding up the computation of the intersection of URIs of any set of datasets. We discuss the speedup obtained by the introduced indexes and algorithms through comparative results and finally we report measurements about connectivity of the LOD cloud that have never been carried out so far.

References

[1]

C. Aggarwal, Y. Xie, and P. S. Yu. Gconnect: A connectivity index for massive disk-resident graphs. Proceedings of the VLDB Endowment, 2(1):862--873, 2009.

Digital Library

[2]

S. Auer, J. Demter, M. Martin, and J. Lehmann. LODStats - an Extensible Framework for High-Performance Dataset Analytics. In Proceedings of EKAW, volume 7603, pages 353--362. 2012.

Digital Library

[3]

N. Bikakis and T. K. Sellis. Exploration and visualization in the web of big linked data: A survey of the state of the art. In LWDM, 2016.

[4]

C. Bizer, P. Boncz, M. L. Brodie, and O. Erling. The meaningful use of big data: four perspectives--four challenges. ACM SIGMOD Record, 40(4):56--60, 2012.

Digital Library

[5]

L. Ding, T. Finin, A. Joshi, R. Pan, R. S. Cost, Y. Peng, P. Reddivari, V. Doshi, and J. Sachs. Swoogle: a search and metadata engine for the semantic web. In Proceedings of CIKM, pages 652--659, 2004.

Digital Library

[6]

M. B. Ellefi, Z. Bellahsene, S. Dietze, and K. Todorov. Dataset recommendation for data linking: an intensional approach. In Proceedingss of ESWC, volume 9678, pages 36--51, 2016.

Digital Library

[7]

A. Harth, J. Umbrich, A. Hogan, and S. Decker. YARS2: A federated repository for querying graph structured data from the web. In ISWC, volume 4825, pages 211--224, 2007.

Digital Library

[8]

J. Hendler. Data integration for heterogenous datasets. Big data, 2(4):205--215, 2014.

[9]

A. Hogan, A. Harth, and S. Decker. Performing object consolidation on the semantic web data graph. In Proceedings 3 of the WWW Workshop I³, 2007.

[10]

A. Hogan, A. Harth, J. Umbrich, S. Kinsella, A. Polleres, and S. Decker. Searching and browsing linked data with swse: The semantic web search engine. Web semantics: science, services and agents on the world wide web, 9(4):365--401, 2011.

Digital Library

[11]

T. Jech. Set theory. Springer Science & Business Media, 2013.

[12]

M. H. Keith Alexander, Richard Cyganiak and J. Zhao. Describing linked datasets with the VoID vocabulary, W3C interest group note, 2011.

[13]

G. Klyne and J. J. Carroll. Resource description framework (rdf): Concepts and abstract syntax. 2006.

[14]

M. Mountantonakis, N. Minadakis, Y. Marketakis, P. Fafalios, and Y. Tzitzikas. Quantifying the connectivity of a semantic warehouse and understanding its evolution over time. IJSWIS, 12(3), 2016.

[15]

M. Nentwig, T. Soru, A.-C. N. Ngomo, and E. Rahm. Linklion: A link repository for the web of data. In The Semantic Web: ESWC Satellite Events, volume 8798, pages 439--443. 2014.

[16]

T. Neumann and G. Weikum. The RDF-3X engine for scalable management of RDF data. The VLDB Journal, 19(1):91--113, 2010.

Digital Library

[17]

D. Oguz, B. Ergenc, S. Yin, O. Dikenelli, and A. Hameurlain. Federated query processing on linked data: a qualitative survey and open challenges. The Knowledge Engineering Review, 30(5):545--563, 2015.

[18]

P. Peng, L. Zou, M. T. Özsu, L. Chen, and D. Zhao. Processing SPARQL queries over distributed RDF graphs. The VLDB Journal, 25(2):243--268, 2016.

Digital Library

[19]

E. Prud' Hommeaux, A. Seaborne, et al. SPARQL query language for RDF. W3C recommendation, 15, 2008.

[20]

L. Rietveld, W. Beek, and S. Schlobach. LOD lab: Experiments at LOD scale. In Proceedings of ISWC, volume 9367, pages 339--355, 2015.

[21]

M. Schmachtenberg, C. Bizer, and H. Paulheim. Adoption of the linked data best practices in different topical domains. In Porceedings of ISWC, volume 8796, pages 245--260. 2014.

Digital Library

[22]

R. Tarjan. Depth-first search and linear graph algorithms. In Twelfth Annual Symposium on Switching and Automata Theory, pages 114--121, 1971.

Digital Library

[23]

Y. Theoharis, Y. Tzitzikas, D. Kotzinos, and V. Christophides. On graph features of semantic web schemas. Knowledge and Data Engineering, 20(5):692--702, 2008.

Digital Library

[24]

G. Tummarello, E. Oren, and R. Delbru. Sindice.com: Weaving the open linked data. In Proceedings of ISWC, volume 4825, pages 547--560, 2007.

Digital Library

[25]

A. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann, and S. Auer. Quality assessment for linked data: A survey. Semantic Web Journal, 7(1):63--93, 2016.

[26]

J. Zobel and A. Moffat. Inverted files for text search engines. ACM computing surveys, 38(2):6, 2006.

Digital Library

[27]

K. Zoumpatianos, S. Idreos, and T. Palpanas. Indexing for interactive exploration of big data series. In Proceedings of the ACM SIGMOD, pages 1555--1566, 2014.

Digital Library

Cited By

Tzitzikas YPapadaki MChatzakis M(2022)A spiral-like method to place in the space (and interact with) too many valuesJournal of Intelligent Information Systems10.1007/s10844-021-00677-258:3(535-559)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10844-021-00677-2
Mountantonakis MTzitzikas Y(2022)LODChain: Strengthen the Connectivity of Your RDF Dataset to the Rest LOD CloudThe Semantic Web – ISWC 202210.1007/978-3-031-19433-7_31(537-555)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19433-7_31
Papadaki MSpyratos NTzitzikas Y(2021)Towards Interactive Analytics over RDF GraphsAlgorithms10.3390/a1402003414:2(34)Online publication date: 25-Jan-2021
https://doi.org/10.3390/a14020034
Show More Cited By

On measuring the lattice of commonalities among several linked datasets
1. Information systems

Recommendations

Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked Datasets
Special Issue on Improving the Veracity and Value of Big Data

Although the ultimate objective of Linked Data is linking and integration, it is not currently evident how connected the current Linked Open Data (LOD) cloud is. In this article, we focus on methods, supported by special indexes and algorithms, for ...
Parallel mining of OWL 2 EL ontology from large linked datasets

Linked Data has become a vast repository with billions of triples available in thousands of datasets. One of the challenges in integrating, querying and reusing the Linked Data is obtaining the ontology to which the datasets conform. Although many ...
Learning Commonalities in RDF
The Semantic Web
Abstract
Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning introduced in the 70’s, which amounts to computing a least general generalization() of such descriptions. It has also ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 9, Issue 12

August 2016

345 pages

ISSN:2150-8097

Editors:
Surajit Chaudhuri
Microsoft Research
,
Jayant Haritsa
I.I.Sc. Bangalore

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 August 2016

Published in PVLDB Volume 9, Issue 12

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
81
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tzitzikas YPapadaki MChatzakis M(2022)A spiral-like method to place in the space (and interact with) too many valuesJournal of Intelligent Information Systems10.1007/s10844-021-00677-258:3(535-559)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s10844-021-00677-2
Mountantonakis MTzitzikas Y(2022)LODChain: Strengthen the Connectivity of Your RDF Dataset to the Rest LOD CloudThe Semantic Web – ISWC 202210.1007/978-3-031-19433-7_31(537-555)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19433-7_31
Papadaki MSpyratos NTzitzikas Y(2021)Towards Interactive Analytics over RDF GraphsAlgorithms10.3390/a1402003414:2(34)Online publication date: 25-Jan-2021
https://doi.org/10.3390/a14020034
Mountantonakis M(2021)Large scale services for connecting and integrating hundreds of linked datasetsACM SIGWEB Newsletter10.1145/3494825.34948282021:Autumn(1-4)Online publication date: 3-Dec-2021
https://dl.acm.org/doi/10.1145/3494825.3494828
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
Papadaki MTzitzikas YSpyratos N(2020)Analytics over RDF GraphsInformation Search, Integration, and Personalization10.1007/978-3-030-44900-1_3(37-52)Online publication date: 27-Mar-2020
https://doi.org/10.1007/978-3-030-44900-1_3
Raad JAcar ESchlobach SKejriwal MSzekely PTroncy R(2019)On the Impact of sameAs on Schema MatchingProceedings of the 10th International Conference on Knowledge Capture10.1145/3360901.3364442(77-84)Online publication date: 23-Sep-2019
https://dl.acm.org/doi/10.1145/3360901.3364442
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
Piscopo ASimperl ELundell BGamalielsson JMorgan LRobles G(2019)What we talk about when we talk about wikidata qualityProceedings of the 15th International Symposium on Open Collaboration10.1145/3306446.3340822(1-11)Online publication date: 20-Aug-2019
https://dl.acm.org/doi/10.1145/3306446.3340822
Dimitrakis ESgontzos KTzitzikas Y(2019)A survey on question answering systems over linked data and documentsJournal of Intelligent Information Systems10.1007/s10844-019-00584-755:2(233-259)Online publication date: 4-Dec-2019
https://doi.org/10.1007/s10844-019-00584-7
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents