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Algorithms for Recollection of Search Terms Based on the Wikipedia Category Structure

机译:基于维基百科类别结构回忆搜索项的算法

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The common user interface for a search engine consists of a text field where the user can enter queries consisting of one or more keywords. Keyword query based search engines work well when the users have a clear vision what they are looking for and are capable of articulating their query using the same terms as indexed. For our multimedia database containing 202,868 items with text descriptions, we supplement such a search engine with a category-based interface whose category structure is tailored to the content of the database. This facilitates browsing and offers the users the possibility to look for named entities, even if they forgot their names. We demonstrate that this approach allows users who fail to recollect the name of named entities to retrieve data with little effort. In all our experiments, it takes 1 query on a category and on average 2.49 clicks, compared to 5.68 queries on the database’s traditional text search engine for a 68.3% success probability or 6.01 queries when the user also turns to Google, for a 97.1% success probability.
机译:用于搜索引擎的公共用户界面包括文本字段,其中用户可以输入由一个或多个关键字组成的查询。基于关键字查询的搜索引擎在用户有清晰的愿景时,他们正在寻找的内容,并且能够使用与索引相同的术语来阐明他们的查询。对于包含具有文本描述的202,868项的多媒体数据库,我们可以使用基于类别的界面来补充此类搜索引擎,其类别结构对数据库的内容量身定制。这有助于浏览并提供用户即使他们忘记了名字,也可以寻找命名实体的可能性。我们展示了这种方法允许用户未能回顾命名实体的名称以检索具有很少的努力的数据。在我们的所有实验中,在一个类别和平均的点击次数上有1个查询,而在数据库的传统文本搜索引擎上的5.68查询相比,在用户转到谷歌时的68.3%的成功概率或6.01查询,为97.1%成功概率。

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