Searching Semantic Associations Based on Virtual Document

  • Chen Wang
  • Xiang Zhang
  • Yongtao Lv
  • Li Ji
  • Peng Wang
Part of the Communications in Computer and Information Science book series (CCIS, volume 406)


As the explosive growth of online linked data, enormous RDF triples are produced every minute in various fields such as health, transportation, chemical, etc. There is an urgent need for an approach to finding and searching semantic association from massive data. However, the complex graph structure of the semantic association brings a great barrier to the process of searching. Transforming the complex graph into text-based structure is a better idea. To characterize the semantics of each association, a virtual document of each association is built with the help of a neighboring operation. A searching model of virtual documents of associations and a ranking schema are also discussed in this paper. Experiments show that our approach is feasible and efficient.


linked data semantic association link pattern virtual document 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aleman-Meza, B., Halaschek-Wiener, C., Arpinar, I.B., Sheth, A.P.: Context-aware Semantic Association Ranking. In: Proceedings of the 1st International Workshop on Semantic Web and Databases, pp. 33–50 (2003)Google Scholar
  2. 2.
    Anyanwu, K., Sheth, A.: p-Queries: Enabling Querying for Semantic Associations on the Semantic Web. In: Proceedings of the 12th International World Wide Web Conference, pp. 690–699 (2003)Google Scholar
  3. 3.
    Kochut, K.J., Janik, M.: SPARQLeR: Extended Sparql for Semantic Association Discovery. In: Proceedings of the 4th European Conference on Semantic Web, pp. 145–159 (2007)Google Scholar
  4. 4.
    Zhang, X., Zhao, C., Wang, P., Zhou, F.: Mining Link Patterns in Linked Data. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 83–94. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Yan, X., Han, J.W.: gSpan: Graph-based Substructure Pattern Mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 721–724 (2002)Google Scholar
  6. 6.
    Le, B.T., Dieng-Kuntz, R., Gandon, F.: On Ontology Matching Problems - for Building a Corporate Semantic Web in a Multi-Communities Organization. In: Proceedings of the 2004 International Conference on Enterprize Information Systems, pp. 236–243 (2004)Google Scholar
  7. 7.
    Lacher, M.S., Groh, G.: Facilitating the Exchange of Explicit Knowledge through Ontology Mappings. In: Proceedings of the 14th Int. FLAIRS Conference, pp. 305–309 (2001)Google Scholar
  8. 8.
    Qu, Y., Hu, W., Cheng, G.: Constructing virtual documents for ontology matching. In: Proceedings of the 15th International Conference on World Wide Web (WWW 2006), pp. 23–31 (2006)Google Scholar
  9. 9.
    Doan, A., Madhavan, J., Dhamankar, R., Domingos, P., Halevy, A.Y.: Learning to Match Ontologies on the Semantic Web. Proceedings of the VLDB Journal 12(4), 303–319 (2003)CrossRefGoogle Scholar
  10. 10.
    James, C.A., Weininger, D., Delany, J.: Daylight theory manual daylight version 4.82. Daylight Chemical Information Systems (2003)Google Scholar
  11. 11.
    Jiang, H., Wang, H., Yu, P.S., Zhou, S.: GString: A Novel Approach for Efficient Search in Graph Databases. In: Proceedings of IEEE 23rd International Conference on Data Engineering, ICDE, pp. 566–575 (2007)Google Scholar
  12. 12.
    Shasha, D., Wang, J.T., Giugno, R.: Algorithmics and applications of tree and graph searching. In: Proceedings of Symposium on Principle of Database Systems, PODS, pp. 39–52 (2002)Google Scholar
  13. 13.
    Yan, X., Yu, P.S., Han, J.: Substructure similarity search in graph databases. In: Proccedings of International Conference on Management of Data-SIGMOD, pp. 766–777 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chen Wang
    • 1
  • Xiang Zhang
    • 2
  • Yongtao Lv
    • 1
  • Li Ji
    • 1
  • Peng Wang
    • 2
  1. 1.College of Software EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations