Iterative Entity Navigation via Co-clustering Semantic Links and Entity Classes

  • Liang Zheng
  • Jiang Xu
  • Jidong Jiang
  • Yuzhong Qu
  • Gong Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)


With the increasing volume of Linked Data, the diverse links and the large amount of linked entities make it difficult for users to traverse RDF data. As semantic links and classes of linked entities are two key aspects to help users navigate, clustering links and classes can offer effective ways of navigating over RDF data. In this paper, we propose a co-clustering approach to provide users with iterative entity navigation. It clusters both links and classes simultaneously utilizing both the relationship between link and class, and the intra-link relationship and intra-class relationship. We evaluate our approach on a real-world data set and the experimental results demonstrate the effectiveness of our approach. A user study is conducted on a prototype system to show that our approach provides useful support for iterative entity navigation.


Entity navigation Semantic link Entity class Co-clustering 



This work is supported in part by the 863 Program under Grant 2015AA015406, in part by the National Science Foundation of China under Grant Nos. 61223003 and 61572247, and in part by the Fundamental Research Funds for the Central Universities. We are also grateful to all the participants in the experiments of this work.


  1. 1.
    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: 3rd International Semantic Web User Interaction Workshop (2006)Google Scholar
  2. 2.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia-a crystallization point for the web of data. J. Web Sem. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  3. 3.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: 7th International Conference on Knowledge Discovery and Data mining, pp. 269–274 (2001)Google Scholar
  4. 4.
    Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: 9th International Conference on Knowledge Discovery and Data mining, pp. 89–98 (2003)Google Scholar
  5. 5.
    Giannakidou, E., Koutsonikola, V.A., Vakali, A., Kompatsiaris, Y.: Co-clustering tags and social data sources. In: 9th International Conference on Web-Age Information Management, pp. 317–324. IEEE (2008)Google Scholar
  6. 6.
    Garcia, R., Gimeno, J.M., Perdrix, F., et al.: Building a usable and accessible semantic web interaction platform. World Wide Web 13(1–2), 143–167 (2010)CrossRefGoogle Scholar
  7. 7.
    Harth, A.: VisiNav: a system for visual search and navigation on web data. J. Web Sem. 8(4), 348–354 (2010)CrossRefGoogle Scholar
  8. 8.
    Heim, P., Ertl, T., Ziegler, J.: Facet graphs: complex semantic querying made easy. In: Aroyo, L., Antoniou, G., Hyvönen, E., Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 288–302. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Lin, D.: An information-theoretic definition of similarity. In: International Conference on Machine Learning, pp. 296–304 (1998)Google Scholar
  10. 10.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  11. 11.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  12. 12.
    Mika, P.: Ontologies are us: a unified model of social networks and semantics. J. Web Sem. 5(1), 5–15 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Popov, I.O., Schraefel, M.C., Hall, W., Shadbolt, N.: Connecting the dots: a multi-pivot approach to data exploration. 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. 553–568. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Wu, J.S., Lai, J.H., Wang, C.D.: A novel co-clustering method with intra-similarities. In: 11th International Conference on Data Mining Workshops, pp. 300–306 (2011)Google Scholar
  15. 15.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1994)Google Scholar
  16. 16.
    Zheng, L., Qu, Y., Jiang, J., Cheng, G.: Facilitating entity navigation through top-k link patterns. In: 14th International Semantic Web Conference, pp. 163–179 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Liang Zheng
    • 1
  • Jiang Xu
    • 1
  • Jidong Jiang
    • 1
  • Yuzhong Qu
    • 1
  • Gong Cheng
    • 1
  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China

Personalised recommendations