Learning Parameters in Entity Relationship Graphs from Ranking Preferences

  • Soumen Chakrabarti
  • Alekh Agarwal
Conference paper

DOI: 10.1007/11871637_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)
Cite this paper as:
Chakrabarti S., Agarwal A. (2006) Learning Parameters in Entity Relationship Graphs from Ranking Preferences. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science, vol 4213. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

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