Learning parameters in entity-relationship graphs from ranking preferences

Chakrabarti, Soumen ; Agarwal, Alekh (2006) Learning parameters in entity-relationship graphs from ranking preferences In: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 18-22, Berlin, Germany.

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Official URL: http://link.springer.com/chapter/10.1007%2F1187163...


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..

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to Springer.
ID Code:100080
Deposited On:12 Feb 2018 12:28
Last Modified:12 Feb 2018 12:28

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