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Analyzing RDF Data in Linked Open Data Cloud using Formal Concept Analysis

  • Received : 2017.05.12
  • Accepted : 2017.06.19
  • Published : 2017.06.30

Abstract

The Linked Open Data(LOD) cloud is quickly becoming one of the largest collections of interlinked datasets and the de facto standard for publishing, sharing and connecting pieces of data on the Web. Data publishers from diverse domains publish their data using Resource Description Framework(RDF) data model and provide SPARQL endpoints to enable querying their data, which enables creating a global, distributed and interconnected dataspace on the LOD cloud. Although it is possible to extract structured data as query results by using SPARQL, users have very poor in analysis and visualization of RDF data from SPARQL query results. Therefore, to tackle this issue, based on Formal Concept Analysis, we propose a novel approach for analyzing and visualizing useful information from the LOD cloud. The RDF data analysis and visualization technique proposed in this paper can be utilized in the field of semantic web data mining by extracting and analyzing the information and knowledge inherent in LOD and supporting classification and visualization.

Keywords

References

  1. C. Bizer, T. Heath, T. Berners-Lee, "Linked Data - The Story So Far," International Journal on Semantic Web and Information Systems, Vol. 5, No. 3, pp. 1-22, March 2009. https://doi.org/10.4018/jswis.2009081901
  2. "2014 Case Study of Korean Linked Open Data," National Information Society Agency, 2014.
  3. SPARQL 1.1 Query Language, http://www.w3.org/TR/sparql11-query/
  4. B. Ganter, R. Wille, "Formal Concept Analysis: Mathematical Foundations," Springer-Verlag, 1999.
  5. P. Teufl, and L. and Gunther Lackner. "Knowledge Extraction from RDF Data with Activation Patterns," Journal of Universal Computer Science, Vol. 17, No. 7, pp. 983-1004, January 2011.
  6. J. Du, H. Wang, Y. Ni, Y. Yu, "HadoopRDF : A Scalable Semantic Data Analytical Engine," Intelligent Computing Theories and Applications, Lecture Notes in Computer Science Vol. 7390, pp. 633-641, July 2012.
  7. D. Colazzo, F. Goasdoue, I. Manolescu, A. Roatis, "RDF analytics: lenses over semantic graphs," Proceedings of the 23rd international conference on World Wide Web Conferences, pp. 467-478, 2014.
  8. E. Ruckhaus, O. Baldizan, M. Vidal, "Analyzing Linked Data Quality with LiQuate," Proceedings of the 12th International OnTheMove (OTM 2013) Conferences, pp. 629-638, 2013.
  9. M. d'Aquin, E. Motta, "Extracting relevant questions to an RDF dataset using formal concept analysis," Proceedings of the 6th international conference on Knowledge capture(K-CAP '11), pp. 121-128, 2011.
  10. S. Sundara, M. Atre, V. Kolovski, S. Das, Z. Wu, E. Chong, J. Srinivasan, "Visualizing large-scale RDF data using Subsets, Summaries, and Sampling in Oracle," Proceedings of the 26th international Conference on Data Eng., pp. 1048-1059, 2010.
  11. P. Bellini, P. Nesi, A. Venturi, "Linked open graph: Browsing multiple SPARQL entry points to build your own LOD views," Journal of Visual Languages & Computing, Vol. 25, No. 6, Author Pierfrancesco Bellini, Year 2014, Vol 25, Issue 6. pp.703-716, December 2014. https://doi.org/10.1016/j.jvlc.2014.10.003
  12. Resource Description Framework, https://www.w3.org/RDF/
  13. SPARQL Endpoints Status, http://sparqles.okfn.org/
  14. M. Arias, J. Fernandez, M. Martinez-Prieto, P. Fuente, "An empirical study of real-world sparql queries," Proceedings of the 20th international conference companion on World Wide Web, pp.305-306, 2011.