Skip to main content
SpringerLink
Log in
Book cover

Transactions on Computational Collective Intelligence XIV pp 180–195Cite as

Visualization of Semantic Data Based on Selected Predicates

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8615))

Abstract

Due to the spreading of semantic technologies, the volume of the datasets that are described in the Resource Description Framework (RDF) is dynamically growing. The RDF framework is suitable for integrating data from heterogeneous sources; however, the resulted datasets can be larger and extremely complex than before, new tools are needed to analyze them. In this paper, we present a method which aims to help to understand the structure of semantic datasets. It can reduce the size and the complexity of a dataset while preserves the selected parts of it. The method consists of a filtering and a compaction phases that are implemented according to the MapReduce distributed programing model to be able to handle large volume of data. The result of the method can be visualized as a labeled directed graph that is suitable to give an overview of the structure of the dataset. It may reveal hidden connections or different kinds of problems related to the completeness and correctness of the data.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.yworks.com/en/products_yed_about.htm

  2. 2.

    Available at http://wiki.dbpedia.org/Downloads38

  3. 3.

    Available at http://download.freebaseapps.com

  4. 4.

    See www.freebase.com/m/07grj

  5. 5.

    See www.freebase.com/m/01rh56d

  6. 6.

    See www.freebase.com/m/01wtsmx

  7. 7.

    See www.freebase.com/m/0plk41m

  8. 8.

    See http://www.freebase.com/m/02pjxr

References

  1. Alexander, K., Hausenblas, M.: Describing linked datasets-on the design and usage of void, the vocabulary of interlinked datasets. In: Linked Data on the Web Workshop (LDOW 09), in conjunction with 18th International World Wide Web Conference (WWW 09) (2009)

    Google Scholar 

  2. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) SWC/ASWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  3. Beckett, D., Barstow, A.: N-triples (2001)

    Google Scholar 

  4. Berners-Lee, T., Hendler, J., Lassila, O., et al.: The semantic web. Sci. Am. 284(5), 28–37 (2001)

    Article  Google Scholar 

  5. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)

    Google Scholar 

  6. Brandes, U., Eiglsperger, M., Lerner, J., Pich, C.: Graph markup language (GraphML). Bibliothek der Universität Konstanz (2010)

    Google Scholar 

  7. Chen, B., Ding, Y. Wang, H. Wild, D.J., Dong, X., Sun, Y., Zhu, Q., Sankaranarayanan, M.: Chem2bio2rdf: a linked open data portal for systems chemical biology. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 232–239. IEEE (2010)

    Google Scholar 

  8. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  9. Deligiannidis, L., Kochut, K.J., Sheth, A.P.: RDF data exploration and visualization. In: Proceedings of the ACM First Workshop on CyberInfrastructure: Information Management in eScience, pp. 39–46. ACM (2007)

    Google Scholar 

  10. Dokulil, J., Katreniaková, J.: Visual exploration of RDF data. In: Geffert, V., Karhumäki, J., Bertoni, A., Preneel, B., Návrat, P., Bieliková, M. (eds.) SOFSEM 2008. LNCS, vol. 4910, pp. 672–683. Springer, Heidelberg (2008)

    Google Scholar 

  11. Duong, T.H., Jo, G., Jung, J.J., Nguyen, N.T.: Complexity analysis of ontology integration methodologies: a comparative study. J. UCS 15(4), 877–897 (2009)

    MATH  MathSciNet  Google Scholar 

  12. Gutierrez, C., Hurtado, C., Mendelzon, A.O.: Foundations of semantic web databases. In Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 95–106. ACM (2004)

    Google Scholar 

  13. Harris, S., Seaborne, A.: Sparql 1.1 query language. Technical report, W3C (2010)

    Google Scholar 

  14. Henzinger, M.R., Henzinger, T.A., Kopke, P.W.: Computing simulations on finite and infinite graphs. In: Proceedings of 36th Annual Symposium on Foundations of Computer Science, 1995, pp. 453–462. IEEE (1995)

    Google Scholar 

  15. Husain, M., Khan, L., Kantarcioglu, M., Thuraisingham, B.: Data intensive query processing for large RDF graphs using cloud computing tools. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 1–10. IEEE (2010)

    Google Scholar 

  16. Lassila, O., Swick, R.R., et al.: Resource description framework (RDF) model and syntax specification (1998)

    Google Scholar 

  17. Micsik, A., Turbucz, S., Tóth, Z.: Browsing and traversing linked data with lodmilla. ERCIM News 2014(96), 35–36 (2014)

    Google Scholar 

  18. Molnár, A.J., Benczúr, A.A., Sidló, C.I.: Flexible and efficient distributed resolution of large entities. In: Lukasiewicz, T., Sali, A. (eds.) FoIKS 2012. LNCS, vol. 7153, pp. 244–263. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Nguyen, N.T.: Inconsistency of knowledge and collective intelligence. Cybern. Syst. Int. J. 39(6), 542–562 (2008)

    Article  MATH  Google Scholar 

  20. Schätzle, A., Neu, A., Lausen, G., Przyjaciel-Zablocki, M.: Large-scale bisimulation of RDF graphs. In: Proceedings of the Fifth Workshop on Semantic Web Information Management, p. 1. ACM (2013)

    Google Scholar 

  21. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)

    Google Scholar 

  22. Voigt, M., Pietschmann, S., Meißner, K.: Towards a semantics-based, end-user-centered information visualization process. In Proceedings of the 3rd International Workshop on Semantic Models for Adaptive Interactive Systems (SEMAIS 2012) (2012)

    Google Scholar 

  23. White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc, Sebastopol (2012)

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by the European Union and the European Social Fund through project FuturICT.hu (grant no.: TAMOP-4.2.2.C-11/1/KONV-2012-0013) and the Hungarian and Vietnamese TET (grant no.: TT_10-1-2011-0645) project.

Author information

Authors and Affiliations

  1. Eötvös Loránd University, Budapest, Hungary

    Gábor Rácz, Gergő Gombos & Attila Kiss

Authors
  1. Gábor Rácz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gergő Gombos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Attila Kiss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gergő Gombos .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Rácz, G., Gombos, G., Kiss, A. (2014). Visualization of Semantic Data Based on Selected Predicates. In: Nguyen, N. (eds) Transactions on Computational Collective Intelligence XIV. Lecture Notes in Computer Science(), vol 8615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44509-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44509-9_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44508-2

  • Online ISBN: 978-3-662-44509-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation