research-article

Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked Datasets

Authors:

Michalis Mountantonakis,

Yannis TzitzikasAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 9, Issue 3

Article No.: 15, Pages 1 - 49

https://doi.org/10.1145/3165713

Published: 27 January 2018 Publication History

Abstract

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 performing measurements related to the connectivity of more than two datasets that are useful in various tasks including (a) Dataset Discovery and Selection; (b) Object Coreference, i.e., for obtaining complete information about a set of entities, including provenance information; (c) Data Quality Assessment and Improvement, i.e., for assessing the connectivity between any set of datasets and monitoring their evolution over time, as well as for estimating data veracity; (d) Dataset Visualizations; and various other tasks. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this article, we introduce indexes (and their construction algorithms) that can speed up 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. For enhancing scalability, we propose parallel index construction algorithms and parallel lattice-based incremental algorithms, we evaluate the achieved speedup using either a single machine or a cluster of machines, and we provide insights regarding the factors that affect efficiency. Finally, we report measurements about the connectivity of the (billion triples-sized) LOD cloud that have never been carried out so far.

References

[1]

Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Sören Auer, and Jens Lehmann. 2013. Crowdsourcing linked data quality assessment. In International Semantic Web Conference. Springer, 260--276.

Digital Library

[2]

Charu Aggarwal, Yan Xie, and Philip S. Yu. 2009. Gconnect: A connectivity index for massive disk-resident graphs. Proc. VLDB Endow. 2, 1 (2009), 862--873.

Digital Library

[3]

Sören Auer, Jan Demter, Michael Martin, and Jens Lehmann. 2012. LODStats--An extensible framework for high-performance dataset analytics. In International Conference on Knowledge Engineering and Knowledge Management. Springer, 353--362.

Digital Library

[4]

Nikos Bikakis and Timos K. Sellis. 2016. Exploration and visualization in the web of big linked data: A survey of the state of the art. In Proceedings of the International Conference on Extending Database Technology/International Conference on Display Technology Workshops (EDBT/ICDT’16), Vol. 1558.

[5]

Christian Bizer, Peter Boncz, Michael L Brodie, and Orri Erling. 2012. The meaningful use of big data: Four perspectives--four challenges. ACM SIGMOD Rec. 40, 4 (2012), 56--60.

Digital Library

[6]

Christoph Böhm, Gerard de Melo, Felix Naumann, and Gerhard Weikum. 2012. LINDA: Distributed web-of-data-scale entity matching. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 2104--2108.

Digital Library

[7]

Vassilis Christophides, Vasilis Efthymiou, and Kostas Stefanidis. 2015. Entity resolution in the web of data. Synth. Lect. Semant. Web 5, 3 (2015), 1--122.

Digital Library

[8]

Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Commun. ACM 51, 1 (2008), 107--113.

Digital Library

[9]

Li Ding, Tim Finin, Anupam Joshi, Rong Pan, R. Scott Cost, Yun Peng, Pavan Reddivari, Vishal Doshi, and Joel Sachs. 2004. Swoogle: A search and metadata engine for the semantic web. In Proceedings of the 13th ACM International Conference on Information and Knowledge Management. ACM, 652--659.

Digital Library

[10]

Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Kevin Murphy, Shaohua Sun, and Wei Zhang. 2014. From data fusion to knowledge fusion. Proc. VLDB Endow. 7, 10 (2014), 881--892.

Digital Library

[11]

Xin Luna Dong and Felix Naumann. 2009. Data fusion: Resolving data conflicts for integration. Proc. VLDB Endow. 2, 2 (2009), 1654--1655.

Digital Library

[12]

Ahmed El-Roby and Ashraf Aboulnaga. 2015. ALEX: Automatic link exploration in linked data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 1839--1853.

Digital Library

[13]

Mohamed Ben Ellefi, Zohra Bellahsene, Stefan Dietze, and Konstantin Todorov. 2016. Dataset recommendation for data linking: An intensional approach. In International Semantic Web Conference. Springer, 36--51.

Digital Library

[14]

José M. Giménez-García, Harsh Thakkar, and Antoine Zimmermann. 2016. Assessing trust with pagerank in the web of data. In International Semantic Web Conference. Springer, 293--307.

[15]

Hugh Glaser, Afraz Jaffri, and Ian Millard. 2009. Managing co-reference on the semantic web. In Proceedings of the WWW2009 Workshop: Linked Data on the Web (LDOW’09).

[16]

Christophe Guéret, Paul Groth, Claus Stadler, and Jens Lehmann. 2012. Assessing linked data mappings using network measures. In The Semantic Web: Research and Applications. Springer, 87--102.

Digital Library

[17]

Harry Halpin and Patrick J. Hayes. 2010. When owl: Sameas isn’t the same: An Analysis of identity links on the semantic web. In Proceedings of the Conference on Linked Data on the Web (LDOW’10).

Digital Library

[18]

Andreas Harth, Jürgen Umbrich, Aidan Hogan, and Stefan Decker. 2007. YARS2: A federated repository for querying graph structured data from the web. In The Semantic Web: Proceedings of the 6th International Semantic Web Conference (ISWC’07). 211--224.

Digital Library

[19]

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

[20]

Aidan Hogan, Andreas Harth, and Stefan Decker. 2007. Performing object consolidation on the semantic web data graph. In Proceedings of the WWW2007 Workshop I3.

[21]

Aidan Hogan, Andreas Harth, Jürgen Umbrich, Sheila Kinsella, Axel Polleres, and Stefan Decker. 2011. Searching and browsing linked data with swse: The semantic web search engine. Web Semant. 9, 4 (2011), 365--401.

Digital Library

[22]

Filip Ilievski, Wouter Beek, Marieke van Erp, Laurens Rietveld, and Stefan Schlobach. 2016. LOTUS: Adaptive text search for big linked data. In International Semantic Web Conference. Springer, 470--485.

Digital Library

[23]

Antoine Isaac and Bernhard Haslhofer. 2013. Europeana linked open data--data. europeana. eu. Semant. Web 4, 3 (2013), 291--297.

Digital Library

[24]

Thomas Jech. 2013. Set Theory. Springer Science 8 Business Media.

[25]

Tobias Käfer, Ahmed Abdelrahman, Jürgen Umbrich, Patrick O’Byrne, and Aidan Hogan. 2013. Observing linked data dynamics. In Extended Semantic Web Conference. Springer, 213--227.

[26]

Tobias Käfer, Jürgen Umbrich, Aidan Hogan, and Axel Polleres. 2012. Towards a dynamic linked data observatory. Proceedings of the Linked Data on the Web at WWW (LDOW’12).

[27]

Jan-Christoph Kalo, Silviu Homoceanu, Jewgeni Rose, and Wolf-Tilo Balke. 2015. Avoiding chinese whispers: Controlling end-to-end join quality in linked open data stores. In Proceedings of the ACM Web Science Conference. ACM.

Digital Library

[28]

U. Kang, Charalampos E. Tsourakakis, and Christos Faloutsos. 2011. PEGASUS: Mining peta-scale graphs. Knowl. Inf. Syst. 27, 2 (2011), 303--325.

Digital Library

[29]

Michael Hausenblas Keith Alexander, Richard Cyganiak and Jun Zhao. 2011. Describing Linked Datasets with the VoID Vocabulary, W3C Interest Group Note. Retrieved from http://www.w3.org/TR/2011/NOTE-void-20110303/.

[30]

Graham Klyne and Jeremy J. Carroll. 2004. Resource description framework (RDF): Concepts and abstract syntax, W3C Recommendation. Retrieved from http://www.w3.org/TR/rdf-concepts/.

[31]

M. Mountantonakis, C. Allocca, P. Fafalios, N. Minadakis, Y. Marketakis, C. Lantzaki, and Y. Tzitzikas. 2014. Extending VoID for expressing the connectivity metrics of a semantic warehouse. In Proceedings of the 1st International Workshop on Dataset Profiling 8 Federated Search for Linked Data (PROFILES’14).

[32]

M. Mountantonakis, N. Minadakis, Y. Marketakis, P. Fafalios, and Y. Tzitzikas. 2016. Quantifying the connectivity of a semantic warehouse and understanding its evolution over time. Int. J. Semant. Web Inf. Syst. 12, 3 (2016), 27–78.

[33]

Michalis Mountantonakis and Yannis Tzitzikas. 2016. On measuring the lattice of commonalities among several linked datasets. Proc. VLDB Endow. 9, 12 (2016), 1101–1112.

Digital Library

[34]

Michalis Mountantonakis and Yannis Tzitzikas. 2017. How linked data can aid machine learning-based tasks. In International Conference on Theory and Practice of Digital Libraries. Springer, 155--168.

[35]

Markus Nentwig, Tommaso Soru, Axel-Cyrille Ngonga Ngomo, and Erhard Rahm. 2014. LinkLion: A link repository for the web of data. In The Semantic Web: ESWC 2014 Satellite Events. Springer, 439--443.

[36]

Thomas Neumann and Gerhard Weikum. 2010. The RDF-3X engine for scalable management of RDF data. VLDB J. 19, 1 (2010), 91--113.

Digital Library

[37]

Damla Oguz, Belgin Ergenc, Shaoyi Yin, Oguz Dikenelli, and Abdelkader Hameurlain. 2015. Federated query processing on linked data: A qualitative survey and open challenges. Knowl. Eng. Rev. 30, 5 (2015), 545--563.

[38]

Laura Papaleo, Nathalie Pernelle, Fatiha Saïs, and Cyril Dumont. 2014. Logical detection of invalid sameas statements in RDF data. In International Conference on Knowledge Engineering and Knowledge Management. Springer, 373--384.

[39]

Norman W. Paton, Klitos Christodoulou, Alvaro A. A. Fernandes, Bijan Parsia, and Cornelia Hedeler. 2012. Pay-as-you-go data integration for linked data: Opportunities, challenges and architectures. In Proceedings of the 4th International Workshop on Semantic Web Information Management. ACM.

Digital Library

[40]

Peng Peng, Lei Zou, M. Tamer Özsu, Lei Chen, and Dongyan Zhao. 2016. Processing SPARQL queries over distributed RDF graphs. VLDB J. 25, 2 (2016), 243–268.

Digital Library

[41]

Eric Prud’ Hommeaux and Andy Seaborne. 2008. SPARQL query language for RDF. W3C Recommend. Retrieved from http://www.w3.org/TR/rdf-sparql-query/.

[42]

Vibhor Rastogi, Ashwin Machanavajjhala, Laukik Chitnis, and Anish Das Sarma. 2013. Finding connected components in map-reduce in logarithmic rounds. In Proceedings of the International Conference on Data Engineering (ICDE’13). IEEE, 50--61.

Digital Library

[43]

Laurens Rietveld, Wouter Beek, and Stefan Schlobach. 2015. LOD lab: Experiments at LOD scale. In International Semantic Web Conference. Springer, 339--355.

[44]

Max Schmachtenberg, Christian Bizer, and Heiko Paulheim. 2014. Adoption of the linked data best practices in different topical domains. In Proceedings of the International Semantic Web Conference (ISWC’14). Springer, 245--260.

Digital Library

[45]

Robert Tarjan. 1971. Depth-first search and linear graph algorithms. In Conference Record of the 1971 12th Annual Symposium on Switching and Automata Theory. IEEE, 114--121.

Digital Library

[46]

Yannis Theoharis, Yannis Tzitzikas, Dimitris Kotzinos, and Vassilis Christophides. 2008. On graph features of semantic web schemas. IEEE Trans. Knowl. Data Eng. 20, 5 (2008), 692--702.

Digital Library

[47]

Giovanni Tummarello, Eyal Oren, and Renaud Delbru. 2007. Sindice.com: Weaving the open linked data. In Proceedings of the International Semantic Web Conference (ISWC’07), Vol. 4825. 547--560.

Digital Library

[48]

Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster computing with working sets.HotCloud 10, 10-10 (2010), 95.

Digital Library

[49]

Amrapali Zaveri, Dimitris Kontokostas, Mohamed A Sherif, Lorenz Bühmann, Mohamed Morsey, Sören Auer, and Jens Lehmann. 2013. User-driven quality evaluation of dbpedia. In Proceedings of the 9th International Conference on Semantic Systems. ACM, 97--104.

Digital Library

[50]

Amrapali Zaveri, Anisa Rula, Andrea Maurino, Ricardo Pietrobon, Jens Lehmann, and Sören Auer. 2016. Quality assessment for linked data: A survey. Semant. Web J. 7, 1 (2016), 63--93.

[51]

Justin Zobel and Alistair Moffat. 2006. Inverted files for text search engines. ACM Comput. Surv. 38, 2 (2006), 6.

Digital Library

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Index Terms

Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked Datasets
1. Information systems
  1. Data management systems
    1. Information integration
  2. World Wide Web
    1. Web data description languages
      1. Semantic web description languages
        Resource Description Framework (RDF)

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Published In

cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 9, Issue 3

Special Issue on Improving the Veracity and Value of Big Data

September 2017

140 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/3183573

Editor:
Tiziana Catarci
Sapienza University of Rome, Rome, Italy

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Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2018

Accepted: 01 November 2017

Revised: 01 November 2017

Received: 01 March 2017

Published in JDIQ Volume 9, Issue 3

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the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI)
European Union's Horizon 2020 research BlueBRIDGE project

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Cited By

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https://doi.org/10.1108/DTA-09-2021-0261
Bernhauer DNečaský MŠkoda PKlímek JSkopal T(2022)Open dataset discovery using context-enhanced similarity searchKnowledge and Information Systems10.1007/s10115-022-01751-z64:12(3265-3291)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10115-022-01751-z
Mountantonakis MTzitzikas Y(2022)How Your Cultural Dataset is Connected to the Rest Linked Open Data?Trandisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage10.1007/978-3-031-20253-7_12(136-148)Online publication date: 24-Nov-2022
https://doi.org/10.1007/978-3-031-20253-7_12
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
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
Berkani NBellatreche LKhouri SOrdonez C(2020)The contribution of linked open data to augment a traditional data warehouseJournal of Intelligent Information Systems10.1007/s10844-020-00594-w55:3(397-421)Online publication date: 1-Dec-2020
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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
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Mountantonakis MTzitzikas Y(2019)Large-scale Semantic Integration of Linked DataACM Computing Surveys10.1145/334555152:5(1-40)Online publication date: 13-Sep-2019
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Mountantonakis MTzitzikas Y(2018)High Performance Methods for Linked Open Data Connectivity AnalyticsInformation10.3390/info90601349:6(134)Online publication date: 3-Jun-2018
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Mountantonakis MTzitzikas Y(2018)LODsyndesis: Global Scale Knowledge ServicesHeritage10.3390/heritage10200231:2(335-348)Online publication date: 17-Nov-2018
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