Cager: a framework for cross-page search

Authors:
Zhumin Chen;Byron J. Gao;Qi Kang
Affiliations:
Shandong University, Jinan, China;Texas State University, San Marcos, TX, USA;Shandong University, Jinan, China
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

Existing search engines have page as the unit of information of retrieval. They typically return a ranked list of pages, each being a search result containing the query keywords. This within-one-page constraint disallows utilization of relationship information that is often available and greatly beneficial. To utilize relationship information and improve search precision, we explore cross-page search, where each answer is a logical page consisting of multiple closely related pages that collectively contain the query keywords. We have implemented a prototype Cager, providing cross-page search and visualization over real dataset.