Record Details

Learning parameters in entity relationship graphs from ranking preferences

DSpace at IIT Bombay

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Title Learning parameters in entity relationship graphs from ranking preferences
 
Creator CHAKRABARTI, S
AGARWAL, A
 
Description 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.
 
Publisher SPRINGER-VERLAG BERLIN
 
Date 2011-10-23T20:56:06Z
2011-12-15T09:11:01Z
2011-10-23T20:56:06Z
2011-12-15T09:11:01Z
2006
 
Type Article; Proceedings Paper
 
Identifier KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2006, PROCEEDINGS,4213,91-102
3-540-45374-1
0302-9743
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15233
http://hdl.handle.net/100/1804
 
Source 10th European Conference on Principle and Practice of Knowledge Discovery in Databases,Berlin, GERMANY,SEP 18-22, 2006
 
Language English