TY - GEN
T1 - Towards rich query interpretation
T2 - 19th International World Wide Web Conference, WWW2010
AU - Agarwal, Ganesh
AU - Kabra, Govind
AU - Chang, Kevin Chen Chuan
PY - 2010/7/20
Y1 - 2010/7/20
N2 - We propose to mine structured query templates from search logs, for enabling rich query interpretation that recognizes both query intents and associated attributes. We formalize the notion of template as a sequence of keywords and domain attributes, and our objective is to discover templates with high precision and recall for matching queries in a domain of interest. Our solution bootstraps from small seed input knowledge to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite QueST network of queries, sites, and templates. We propose a probabilistic inferencing framework based on the dual metrics of precision and recall- and we show that the dual inferencing correspond respectively to the random walks in backward and forward directions. We deployed and tested our algorithm over a real-world search log of 15 million queries. The algorithm achieved accuracy of as high as 90% (on F-measure), with little seed knowledge and even with incomplete domain schema.
AB - We propose to mine structured query templates from search logs, for enabling rich query interpretation that recognizes both query intents and associated attributes. We formalize the notion of template as a sequence of keywords and domain attributes, and our objective is to discover templates with high precision and recall for matching queries in a domain of interest. Our solution bootstraps from small seed input knowledge to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite QueST network of queries, sites, and templates. We propose a probabilistic inferencing framework based on the dual metrics of precision and recall- and we show that the dual inferencing correspond respectively to the random walks in backward and forward directions. We deployed and tested our algorithm over a real-world search log of 15 million queries. The algorithm achieved accuracy of as high as 90% (on F-measure), with little seed knowledge and even with incomplete domain schema.
KW - query attributes
KW - query intents
KW - query templates
KW - search log mining
UR - http://www.scopus.com/inward/record.url?scp=77954575160&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954575160&partnerID=8YFLogxK
U2 - 10.1145/1772690.1772692
DO - 10.1145/1772690.1772692
M3 - Conference contribution
AN - SCOPUS:77954575160
SN - 9781605587998
T3 - Proceedings of the 19th International Conference on World Wide Web, WWW '10
SP - 1
EP - 10
BT - Proceedings of the 19th International Conference on World Wide Web, WWW '10
Y2 - 26 April 2010 through 30 April 2010
ER -