Learning Parameters in Entity Relationship Graphs from Ranking Preferences

  • Soumen Chakrabarti
  • Alekh Agarwal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

Semi-structured entity-relation (ER) data graphs have diverse node and edge types representing entities (paper, person, company) and relations (wrote, works for). In addition, nodes contain text snippets. Extending from vector-space information retrieval, we wish to automatically learn ranking function for searching such typed graphs. User input is in the form of a partial preference order between pairs of nodes, associated with a query. We present a unified model for ranking in ER graphs, and propose an algorithm to learn the parameters of the model. Experiments with carefully-controlled synthetic data as well as real data (garnered using CiteSeer, DBLP and Google Scholar) show that our algorithm can satisfy training preferences and generalize to test preferences, and estimate meaningful model parameters that represent the relative importance of ER types.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agarwal, S., Cortes, C., Herbrich, R. (eds.): Learning to Rank. NIPS Workshop (2005)Google Scholar
  2. 2.
    Balmin, A., Hristidis, V., Papakonstantinou, Y.: Authority-based keyword queries in databases using ObjectRank. In: VLDB, Toronto (2004)Google Scholar
  3. 3.
    Benson, S.J., Moré, J.J.: A limited memory variable metric method for bound constraint minimization. Technical Report ANL/MCS-P909-0901, Argonne National Laboratory (2001)Google Scholar
  4. 4.
    Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., Sudarshan, S.: Keyword searching and browsing in databases using BANKS. In: ICDE. IEEE, Los Alamitos (2002)Google Scholar
  5. 5.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: WWW Conference (1998)Google Scholar
  6. 6.
    Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: A recursive model for graph mining. In: ICDM. SIAM, Philadelphia (2004)Google Scholar
  7. 7.
    Chang, H., Cohn, D., McCallum, A.: Creating customized authority lists. In: ICML (2000)Google Scholar
  8. 8.
    Cohn, D., Hofmann, T.: The missing link — a probabilistic model of document content and hypertext connectivity. In: NIPS (2001)Google Scholar
  9. 9.
    Diligenti, M., Gori, M., Maggini, M.: Learning Web page scores by error back-propagation. In: IJCAI (2005)Google Scholar
  10. 10.
    Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: XRANK: Ranked keyword search over XML documents. In: SIGMOD Conference, pp. 16–27 (2003)Google Scholar
  11. 11.
    Haveliwala, T.H.: Topic-sensitive PageRank. In: WWW, pp. 517–526 (2002)Google Scholar
  12. 12.
    Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: International Conference on Artificial Neural Networks, pp. 97–102 (1999)Google Scholar
  13. 13.
    Jeh, G., Widom, J.: Scaling personalized web search. In: WWW Conference, pp. 271–279 (2003)Google Scholar
  14. 14.
    Joachims, T.: Optimizing search engines using clickthrough data. In: SIGKDD Conference. ACM, New York (2002)Google Scholar
  15. 15.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. JACM 46(5), 604–632 (1999)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
  17. 17.
    Nie, Z., Zhang, Y., Wen, J.-R., Ma, W.-Y.: Object-level ranking: Bringing order to Web objects. In: WWW Conference, pp. 567–574 (2005)Google Scholar
  18. 18.
    Richardson, M., Domingos, P.: The intelligent surfer: Probabilistic combination of link and content information in pagerank. In: NIPS, vol. 14, pp. 1441–1448 (2002)Google Scholar
  19. 19.
    Silva, I., Ribeiro-Neto, B., Calado, P., Moura, E., Ziviani, N.: Link-based and content-based evidential information in a belief network model. In: SIGIR Conference, pp. 96–103 (2000)Google Scholar
  20. 20.
    Tsoi, A.C., Morini, G., Scarselli, F., Hagenbuchner, M., Maggini, M.: Adaptive ranking of web pages. In: WWW Conference, pp. 356–365 (2003)Google Scholar
  21. 21.
    Turtle, H.R., Croft, W.B.: Evaluation of an inference network-based retrieval model. Transactions on Information Systems 9(3), 187–222 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Soumen Chakrabarti
    • 1
  • Alekh Agarwal
    • 1
  1. 1.IIT Bombay 

Personalised recommendations