What SPARQL Query Logs Tell and Do Not Tell About Semantic Relatedness in LOD

Or: The Unsuccessful Attempt to Improve the Browsing Experience of DBpedia by Exploiting Query Logs
  • Jochen Huelss
  • Heiko PaulheimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9341)


Linked Open Data browsers nowadays usually list facts about entities, but they typically do not respect the relatedness of those facts. At the same time, query logs from LOD datasets hold information about which facts are typically queried in conjunction, and should thus provide a notion of intra-fact relatedness. In this paper, we examine the hypothesis how query logs can be used to improve the display of information from DBpedia, by grouping presumably related facts together. The basic assumption is that properties which frequently co-occur in SPARQL queries are highly semantically related, so that co-occurence in query logs can be used for visual grouping of statements in a Linked Data browser. A user study, however, shows that the grouped display is not significantly better than simple baselines, such as the alphabetical ordering used by the standard DBpedia Linked Data interface. A deeper analysis shows that the basic assumption can be proven wrong, i.e., co-occurrence in query logs is actually not a good proxy for semantic relatedness of statements.


Semantic relatedness Linked Open Data Linked data browsers Query log mining DBpedia 


  1. 1.
    Berendt, B., Hollink, L., Hollink, V., Luczak-Rösch, M., Möller, K., Vallet, D.: Usage analysis and the web of data. In: ACM SIGIR Forum. vol. 45, pp. 63–69. ACM (2011)CrossRefGoogle Scholar
  2. 2.
    Berners-Lee, T., Chen, Y., Chilton, L., Connolly, D., Dhanaraj, R., Hollenbach, J., Lerer, A., Sheets, D.: Tabulator: exploring and analyzing linked data on the semantic web. In: Proceedings of the 3rd International Semantic Web User Interaction Workshop (SWUI2006) at the 5th ISWC Conference, Athens, USA (2006)Google Scholar
  3. 3.
    Cheng, G., Tran, T., Qu, Y.: RELIN: relatedness and informativeness-based centrality for entity summarization. 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. 114–129. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  4. 4.
    Dadzie, A.S., Rowe, M.: Approaches to visualising linked data: a survey. Semant. Web 2(2), 89–124 (2011)Google Scholar
  5. 5.
    Delbru, R., Toupikov, N., Catasta, M., Tummarello, G., Decker, S.: Hierarchical link analysis for ranking web data. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 225–239. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  6. 6.
    Ding, L., Pan, R., Finin, T.W., Joshi, A., Peng, Y., Kolari, P.: Finding and ranking knowledge on the semantic web. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 156–170. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  7. 7.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231, Portland, USA (1996)Google Scholar
  8. 8.
    García, R., Paulheim, H., Di Maio, P.: Special issue on semantic web interfaces. Semant. Web 6(8), 213–214 (2015)Google Scholar
  9. 9.
    Kirchberg, M., Ko, R., Lee, B.S.: From linked data to relevant data - time is the essence. In: Proceedings of the 1st International Workshop on Usage Analysis and the Web of Data (USEWOD2011) at the 20th WWW Conference, Hyderabad, India (2011)Google Scholar
  10. 10.
    Van der Laan, M., Pollard, K., Bryan, J.: A new partitioning around medoids algorithm. J. Stat. Comput. Simul. 73(8), 575–584 (2003)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., et al.: Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web 6(2), 167–195 (2015)Google Scholar
  12. 12.
    Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81 (1956)CrossRefGoogle Scholar
  13. 13.
    Möller, K., Hausenblas, M., Cyganiak, R., Handschuh, S.: Learning from linked open data usage: patterns & metrics. In: Proceedings of the 2nd Web Science Conference (WebSci10), Raleigh, USA (2010)Google Scholar
  14. 14.
    Paulheim, H.: Improving the usability of integrated applications by using interactive visualizations of linked data. In: Proceedings of the ACM International Conference on Web Intelligence, Mining and Semantics, Sogndal, Norway (2011)Google Scholar
  15. 15.
    Schmachtenberg, M., Bizer, C., Paulheim, H.: Adoption of the linked data best practices in different topical domains. In: Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 245–260. Springer, Heidelberg (2014) Google Scholar
  16. 16.
    Seeliger, A., Paulheim, H.: A semantic browser for linked open data. In: Proceedings of the Semantic Web Challenge at the 11th ISWC Conference, Boston, USA (2012)Google Scholar
  17. 17.
    Thalhammer, A., Toma, I., Roa-Valverde, A., Fensel, D.: Leveraging usage data for linked data movie entity summarization. In: Proceedings of the 2nd International Workshop on Usage Analysis and the Web of Data (USEWOD2012) at the 21st WWW Conference, Lyon, France (2012)Google Scholar
  18. 18.
    Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Witten, I., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany

Personalised recommendations