Cager: a framework for cross-page search

Zhumin Chen, Byron J. Gao, Qi Kang
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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.
跨页面搜索的框架
现有的搜索引擎以页面作为检索信息的单位。它们通常返回一个页面排序列表,每个页面都是包含查询关键字的搜索结果。这种单页约束不允许使用通常可用且非常有益的关系信息。为了利用关系信息并提高搜索精度,我们探索了跨页面搜索,其中每个答案是由多个紧密相关的页面组成的逻辑页面,这些页面共同包含查询关键字。我们已经实现了一个原型Cager,提供真实数据集的跨页面搜索和可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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