Skip to main content
SpringerLink
Log in

RDF Data Clustering

  • Conference paper
Business Information Systems Workshops (BIS 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 160))

Included in the following conference series:

Abstract

The Web is evolving from a Web of Documents to a Web of Data. Meanwhile, the development of Semantic Web applications opens the way for addressing complex information needs. In this scenario, clustering is identified as a crucial task for semantic mashups. After a thorough review of RDF clustering techniques, the paper addresses the open issues within the efficient exploitation of the knowledge contained in RDF data sources. Then, first promising attempts in exploring the applicability of community detection algorithms for RDF clustering are reported.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Berners-Lee, T.: Linked data (2006), http://www.w3.org/designissues/linkeddata.html

  2. Hausenblas, M., Halb, W., Raimond, Y., Heath, T.: What is the size of the semantic web? In: Proceedings of I-Semantics, pp. 9–16 (2008)

    Google Scholar 

  3. Bizer, C., Heath, T., Idehen, K., Berners-Lee, T.: Linked data on the web (ldow2008). In: Proceedings of the 17th International Conference on World Wide Web, pp. 1265–1266. ACM (2008)

    Google Scholar 

  4. Tran, T., Wang, H., Haase, P.: Hermes: Data web search on a pay-as-you-go integration infrastructure. Web Semantics: Science, Services and Agents on the World Wide Web 7(3), 189–203 (2009)

    Article  Google Scholar 

  5. Zeng, K., Yang, J., Wang, H., Shao, B., Wang, Z.: A distributed graph engine for web scale rdf data. In: Proceedings of the 39th International Conference on Very Large Data Bases, pp. 265–276. VLDB Endowment (2013)

    Google Scholar 

  6. Kaushik, R., Shenoy, P., Bohannon, P., Gudes, E.: Exploiting local similarity for indexing paths in graph-structured data. In: Proceedings of the 18th International Conference on Data Engineering, pp. 129–140. IEEE (2002)

    Google Scholar 

  7. Wu, A.Y., Garland, M., Han, J.: Mining scale-free networks using geodesic clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 719–724. ACM (2004)

    Google Scholar 

  8. Konrath, M., Gottron, T., Staab, S., Scherp, A.: Schemexefficient construction of a data catalogue by stream-based indexing of linked data. Web Semantics: Science, Services and Agents on the World Wide Web 16, 52–58 (2012)

    Article  Google Scholar 

  9. Böhm, C., Lorey, J., Naumann, F.: Creating void descriptions for web-scale data. Web Semantics: Science, Services and Agents on the World Wide Web 9(3), 339–345 (2011)

    Article  Google Scholar 

  10. Algergawy, A., Massmann, S., Rahm, E.: A clustering-based approach for large-scale ontology matching. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 415–428. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010)

    Article  Google Scholar 

  12. W3C: http://www.w3.org/tr/r2rml/ (September 27, 2012)

  13. W3C: http://www.w3.org/tr/rdfa-lite/ (June 07, 2012)

  14. Augenstein, I., Padó, S., Rudolph, S.: LODifier: Generating linked data from unstructured text. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 210–224. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  15. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (2008)

    Google Scholar 

  16. Tran, T., Cimiano, P., Rudolph, S., Studer, R.: Ontology-based interpretation of keywords for semantic search. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 523–536. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Evans, T., Lambiotte, R.: Line graphs, link partitions, and overlapping communities. Physical Review E 80(1), 016105 (2009)

    Google Scholar 

  18. Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)

    Article  Google Scholar 

  19. Schmidt, M., Hornung, T., Lausen, G., Pinkel, C.: Sp2bench: a sparql performance benchmark. In: IEEE 25th International Conference on Data Engineering, ICDE 2009, pp. 222–233. IEEE (2009)

    Google Scholar 

  20. Ravasz, E., Somera, A.L., Mongru, D.A., Oltvai, Z.N., Barabási, A.L.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)

    Article  Google Scholar 

  21. Fanizzi, N., d’Amato, C.: A hierarchical clustering method for semantic knowledge bases. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS (LNAI), vol. 4694, pp. 653–660. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  22. Grimnes, G.A., Edwards, P., Preece, A.D.: Instance based clustering of semantic web resources. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 303–317. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  23. Grimnes, G.A., Edwards, P., Preece, A.D.: Learning meta-descriptions of the FOAF network. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 152–165. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  24. Lösch, U., Bloehdorn, S., Rettinger, A.: Graph kernels for RDF data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 134–148. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Maedche, A., Zacharias, V.: Clustering ontology-based metadata in the semantic web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 348–360. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  26. Delteil, A., Faron-Zucker, C., Dieng, R.: Learning ontologies from rdf annotations. In: Workshop on Ontology Learning (2001)

    Google Scholar 

  27. Rattigan, M.J., Maier, M., Jensen, D.: Graph clustering with network structure indices. In: Proceedings of the 24th International Conference on Machine Learning, pp. 783–790. ACM (2007)

    Google Scholar 

  28. Alzogbi, A., Lausen, G.: Similar structures inside rdf-graphs (2013)

    Google Scholar 

  29. Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proceedings of the VLDB Endowment 2(1), 718–729 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SisInfLab & DEI, Politecnico di Bari, Via Orabona 4, 70125, Bari, Italy

    Silvia Giannini

Authors
  1. Silvia Giannini
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information Systems, Poznań University of Economics, Poznań, Poland

    Witold Abramowicz

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Giannini, S. (2013). RDF Data Clustering. In: Abramowicz, W. (eds) Business Information Systems Workshops. BIS 2013. Lecture Notes in Business Information Processing, vol 160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41687-3_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41687-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41686-6

  • Online ISBN: 978-3-642-41687-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation