Discovering Missing Links in Large-Scale Linked Data

  • Nam Hau
  • Ryutaro Ichise
  • Bac Le
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7803)


The explosion of linked data is creating sparse connection networks, primarily because more and more missing links among difference data sources are resulting from asynchronous and independent database development. DHR was proposed in other research to discover these links.However, DHR has limitations in a distributed environment. For example, while deploying on a distributed SPARQL server, the data transfer usually causes overhead on the network. Therefore, we propose a new method of detecting a missing link based on DHR. The method consists of two stages: finding the frequent graph and matching the similarity. In this paper, we enhance some features in the two stages to reduce the data flow before querying. We conduct an experiment using geographic data sources with a large number of triples to discover the missing links and compare the accuracy of our proposed matching method with DHR and the primitive mix similarity method. The experimental results show that our method can reduce a large amount of data flow on a network and increase the accuracy of discovering missing links.


graph mining linked data link prediction distributed RDF data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Le, N.-T., Ichise, R., Le, H.-B.: Detecting Missing Relations in Geographic Data. In: Proceedings of the 4th International Conference on Advances in Semantic Processing, pp. 61–68 (2010)Google Scholar
  2. 2.
    Raimond, Y., Sutton, C., Sandler, M.: Automatic Interlinking of Music Dataset on Semantic Web. In: Proceedings of Linked Data on the Web (2008)Google Scholar
  3. 3.
    Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and Maintaining Links on the Web of Data. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 650–665. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Isele, R., Jeentzech, A., Bizer, C.: Silk Server - Adding Missing Links While Consuming Linked Data. In: Proceedings of the 9th International Semantic Web Conference, pp. 650–665 (2010)Google Scholar
  5. 5.
    Ngomo, N., Auer, S.: LIMES — A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 2312–2317 (2011)Google Scholar
  6. 6.
    Nguyen, N.B., Ho, T.-B.: A Mixed Similarity Measure in Near-Linear Computational Complexity for Distance-Based Methods. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 211–220. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Ichise, R.: An Analysis of Multiple Similarity Measures for Ontology Mapping Problem. International Journal of Semantic Computing 4(1), 103–122 (2010)zbMATHCrossRefGoogle Scholar
  8. 8.
    Cohen, W.W., Ravikumar, P.D., Fienberg, S.E.: A Comparison of String Distance Metrics for Name-Matching Tasks. In: Proceedings of IJCAI 2003 Workshop on Information Integration on the Web, pp. 73–78 (2003)Google Scholar
  9. 9.
    Christen, P.: A Comparison of Personal Name Matching: Techniques and Practical Issues. In: Proceedings of the 6th IEEE International Conference on Data Mining, pp. 290–294 (2006)Google Scholar
  10. 10.
    Wick, M.: The GeoNames geographical database,
  11. 11.
    DBpedia Team, The DBpedia database (2009),
  12. 12.
    Tauberer, J.: The U.S. census data,
  13. 13.
    CIA Factbook D2R Server, The World Factbook database,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nam Hau
    • 1
  • Ryutaro Ichise
    • 2
  • Bac Le
    • 3
  1. 1.University of TechnologyHo Chi Minh CityVietnam
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.National Science UniversityHo Chi Minh CityVietnam

Personalised recommendations