Link Prediction in Linked Data of Interspecies Interactions Using Hybrid Recommendation Approach

  • Rathachai Chawuthai
  • Hideaki Takeda
  • Tsuyoshi Hosoya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8943)


Linked Open Data for ACademia (LODAC) together with National Museum of Nature and Science have started collecting linked data of interspecies interaction and making link prediction for future observations. The initial data is very sparse and disconnected, making it very difficult to predict potential missing links using only one prediction model alone. In this paper, we introduce Link Prediction in Interspecies Interaction network (LPII) to solve this problem using hybrid recommendation approach. Our prediction model is a combination of three scoring functions, and takes into account collaborative filtering, community structure, and biological classification. We have found our approach, LPII, to be more accurate than other combinations of scoring functions. Using significance testing, we confirm that these three scoring functions are significant for LPII and they play different roles depending on the conditions of linked data. This shows that LPII can be applied to deal with other real-world situations of link prediction.


Biological classification Collaborative filtering Community structure Hybrid recommendation approach Interspecies interaction Linked data Link prediction 


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  1. 1.
    Christian, B., Heath, T., Lee, B.T.: Linked data-the story so far. International Journal on Semantic Web and Information Systems, 1–22 (2009)Google Scholar
  2. 2.
    Minami, Y., et al.: Towards a data hub for biodiversity with LOD. In: Takeda, H., Qu, Y., Mizoguchi, R., Kitamura, Y. (eds.) JIST 2012. LNCS, vol. 7774, pp. 356–361. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Katumoto, K.: List of fungi recorded in Japan. Kanto Branch of the Mycological Society of Japan (2010)Google Scholar
  4. 4.
    Huang, Z., Li, X., Chen, H.: Link prediction approach to collaborative filtering. In: The 5th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM (2005)Google Scholar
  5. 5.
    Feng, X., Zhao, J.C., Xu, K.: Link prediction in complex networks: a clustering perspective. Eur. Phys. J. B 85(1–3) (2012)Google Scholar
  6. 6.
    Peterson, K.R., et al.: Cophylogeny and biogeography of the fungal parasite Cyttaria and its host Nothofagus, southern beech. Mycologia 102(6), 1417–1425 (2010)CrossRefGoogle Scholar
  7. 7.
    Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)CrossRefGoogle Scholar
  8. 8.
    Lu, L., Zhou, T.: Link prediction in complex networks: A survey. Physica A (Elsevier) 390(6), 1150–1170 (2011)CrossRefGoogle Scholar
  9. 9.
    Hamers, L., Hemeryck, Y., et al.: Similarity measures in scientometric research: the Jaccard index versus Salton’s cosine formula. Information Processing & Management 25(3), 315–318 (1989)CrossRefGoogle Scholar
  10. 10.
    Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. Biologiske 5, 1–34 (1948)Google Scholar
  11. 11.
    Zhou, T., et al.: Predicting missing links via local information. The European Physical Journal B 71(4), 623–630 (2009)CrossRefzbMATHGoogle Scholar
  12. 12.
    Huang, C.L., Lin, C.W.: Collaborative and content-based recommender system for social bookmarking website. World Academy of Science, Engineering and Technology 68, 748–753 (2010)Google Scholar
  13. 13.
    Newman, M.E.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  14. 14.
    Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 191–218 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Newman, M.E.: Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321–330 (2004)CrossRefGoogle Scholar
  16. 16.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences 105(4), 1118–1123 (2008)CrossRefGoogle Scholar
  17. 17.
    Lowd, D., Domingos, P.: Naive Bayes models for probability estimation. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 529–536. ACM (2005)Google Scholar
  18. 18.
    Rojsattarat, E., Soonthornphisaj, N.: Hybrid recommendation: combining content-based prediction and collaborative filtering. In: Liu, J., Cheung, Y., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 337–344. Springer, Heidelberg (2003)Google Scholar
  19. 19.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of statistical learning. Springer series in statistics. Springer, New York (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Kim, J., Choy, M., Kim, D., Kang, U.: Link prediction based on generalized cluster information. In: WWW 2014 Companion, pp. 317–318 (2014)Google Scholar
  21. 21.
    Roddick, J.F., Hornsby, K., Vries, D.: A unifying semantic distance model for determining the similarity of attribute values. In: Proceedings of the 26th Australasian Computer Science Conference, vol. 16, pp. 111–118 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Rathachai Chawuthai
    • 1
    • 2
  • Hideaki Takeda
    • 2
  • Tsuyoshi Hosoya
    • 3
  1. 1.The Graduate University for Advanced StudiesKanagawaJapan
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.National Museum of Nature and ScienceTokyoJapan

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