Skip to main content
SpringerLink
Log in

Structure Inference for Linked Data Sources Using Clustering

  • Chapter
  • First Online:
Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 8990))

Abstract

Linked Data (LD) overlays the World Wide Web of documents with a Web of Data. This is becoming significant as shown in the growth of LD repositories available as part of the Linked Open Data (LOD) cloud. At the instance-level, LD sources use a combination of terms from various vocabularies, expressed as RDFS/OWL, to describe data and publish it to the Web. However, LD sources do not organise data to conform to a specific structure analogous to a relational schema; instead data can adhere to multiple vocabularies. Expressing SPARQL queries over LD sources – usually over a SPARQL endpoint that is presented to the user – requires knowledge of the predicates used so as to allow queries to express user requirements as graph patterns. Although LD provides low barriers to data publication using a single language (i.e., RDF), sources organise data with different structures and terminologies. This paper describes an approach to automatically derive structural summaries over instance-level data expressed as RDF triples. The technique builds on a hierarchical clustering algorithm that organises RDF instance-level data into groups that are then utilised to infer a structural summary over a LD source. The resulting structural summaries are expressed in the form of classes, properties and, relationships. Our experimental evaluation shows good results when applied to different types of LD sources.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://lod-cloud.net.

  2. 2.

    See Vocabulary of Interlinked Datasets: http://www.w3.org/TR/void/.

  3. 3.

    For more statistics, see http://www4.wiwiss.fu-berlin.de/lodcloud/state/.

  4. 4.

    Is the opposite of a similarity.

  5. 5.

    Observing the vocabularies listed by the Linked Open Vocabularies (LOV) project: http://lov.okfn.org/dataset/lov/.

  6. 6.

    http://stats.lod2.eu/.

References

  1. Arenas, M., Gutierrez, C., Pérez, J.: Foundations of RDF databases. In: Tessaris, S., Franconi, E., Eiter, T., Gutierrez, C., Handschuh, S., Rousset, M.-C., Schmidt, R.A. (eds.) Reasoning Web. LNCS, vol. 5689, pp. 158–204. Springer, Heidelberg (2009)

    Google Scholar 

  2. Bizer, C., Cyganiak, R.: D2r server - publishing relational databases on the semantic web. In: 5th International Semantic Web Conference, p. 26 (2006)

    Google Scholar 

  3. Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)

    Article  Google Scholar 

  4. Fahad, M.: Er2owl: generating owl ontology from er diagram. In: Shi, Z., Mercier-Laurent, E., Leake, D. (eds.) Intelligent Information Processing IV. IFIP, vol. 288, pp. 28–37. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Franklin, M.J., Halevy, A.Y., Maier, D.: From databases to dataspaces: a new abstraction for information management. SIGMOD Rec. 34(4), 27–33 (2005)

    Article  Google Scholar 

  6. Goldman, R., Widom, J.: Dataguides: enabling query formulation and optimization in semistructured databases. In: Proceedings of the 23rd International Conference on Very Large Data Bases, pp. 436–445. Morgan Kaufmann Publishers Inc. (1997)

    Google Scholar 

  7. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2–3), 107–145 (2001)

    Article  MATH  Google Scholar 

  8. Harth, A., Hose, K., Karnstedt, M., Polleres, A., Sattler, K.-U., Umbrich, J.: Data summaries for on-demand queries over linked data. In: WWW, pp. 411–420 (2010)

    Google Scholar 

  9. Heath, T., Bizer, C.: Linked Data: evolving the web into a global data space. In: Synthesis Lectures on the Semantic Web. Morgan & Claypool Publishers (2011)

    Google Scholar 

  10. Hogan, A., Harth, A., Umbrich, J., Kinsella, S., Polleres, A., Decker, S.: Searching and browsing linked data with swse: the semantic web search engine. J. Web Sem. 9(4), 365–401 (2011)

    Article  Google Scholar 

  11. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience, New York (1990)

    Book  Google Scholar 

  12. Klyne, G., Carroll, J.J.: Resource description framework (RDF): concepts and abstract syntax. Technical report, W3C (2004)

    Google Scholar 

  13. Konrath, M., Gottron, T., Staab, S., Scherp, A.: Schemex - efficient construction of a data catalogue by stream-based indexing of linked data. J. Web Sem. 16, 52–58 (2012)

    Article  Google Scholar 

  14. Larsen, B., Aone, C.: Fast and effective text mining using linear-time document clustering. In: KDD, pp. 16–22 (1999)

    Google Scholar 

  15. Ravi Bhushan Mishra and Sandeep Kumar: Semantic web reasoners and languages. Artif. Intell. Rev. 35(4), 339–368 (2011)

    Article  Google Scholar 

  16. Paton, N.W., Christodoulou, K., Fernandes, A.A.A., Parsia, B., Hedeler, C.: 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, SWIM 2012, pp. 3:1–3:8. ACM (2012)

    Google Scholar 

  17. Prasser, F., Kemper, A., Kuhn, K.A.: Efficient distributed query processing for autonomous RDF databases. In: Proceedings of the 15th International Conference on Extending Database Technology, EDBT 2012, pp. 372–383. ACM (2012)

    Google Scholar 

  18. Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF. W3C Recommendation 4, 1–106 (2008)

    Google Scholar 

  19. Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  20. Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: optimization techniques for federated query processing on linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  21. Umbrich, J., Hose, K., Karnstedt, M., Harth, A., Polleres, A.: Comparing data summaries for processing live queries over linked data. World Wide Web 14(5–6), 495–544 (2011)

    Article  Google Scholar 

  22. Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: CIKM, pp. 515–524 (2002)

    Google Scholar 

  24. Zong, N., Im, D.-H., Yang, S.-K., Namgoong, H., Kim, H.-G.: Dynamic generation of concepts hierarchies for knowledge discovering in bio-medical linked data sets. In: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2012, pp. 12:1–12:5. ACM (2012)

    Google Scholar 

Download references

Acknowledgement

Klitos Christodoulou has been supported by funding from the UK Engineering and Physical Sciences Research council, whose support we are pleased to acknowledge.

Author information

Authors and Affiliations

  1. School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Klitos Christodoulou, Norman W. Paton & Alvaro A. A. Fernandes

Authors
  1. Klitos Christodoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Norman W. Paton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alvaro A. A. Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Klitos Christodoulou .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. University of Brescia, Brescia, Italy

    Devis Bianchini

  5. University of Brescia, Brescia, Italy

    Valeria De Antonellis

  6. University of Rome III, Rome, Italy

    Roberto De Virgilio

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Christodoulou, K., Paton, N.W., Fernandes, A.A.A. (2015). Structure Inference for Linked Data Sources Using Clustering. In: Hameurlain, A., Küng, J., Wagner, R., Bianchini, D., De Antonellis, V., De Virgilio, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX. Lecture Notes in Computer Science(), vol 8990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46562-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46562-2_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46561-5

  • Online ISBN: 978-3-662-46562-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation