Mining Web search engines for query suggestion
Mining Web search engines for query suggestion
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DOI：
10.1002/cpe.1689
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Zheng Xu;Xiangfeng Luo;Jie Yu;Weimin Xu
中科院分区：
文献类型：
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作者：
Zheng Xu;Xiangfeng Luo;Jie Yu;Weimin Xu
对Web搜索引擎的查询通常是简短和模糊的，不能满足用户有效检索相关Web页面的信息需求。为了解决这个问题，大多数搜索引擎都实现了查询建议。然而，现有的方法并没有适当地利用准确性和计算复杂性之间的矛盾（例如b谷歌的“Search related to”和雅虎的“Also Try”）。本文从查询的搜索结果中提取推荐词，适当地保证了查询建议的实时性。基于语义相似度的词排序方案给出了一个词列表作为查询建议结果，保证了查询建议的准确性。此外，实验结果表明，与b谷歌和Yahoo等流行的Web搜索引擎相比，该方法显著提高了查询建议的质量。最后，进行了一个离线实验，比较了片段在捕获文档中单词数量方面的准确性，增加了本文提出的方法的置信度。版权所有©2010 John Wiley & Sons， Ltd
Queries to Web search engines are usually short and ambiguous, which provides insufficient information needs of users for effectively retrieving relevant Web pages. To address this problem, query suggestion is implemented by most search engines. However, existing methods do not leverage the contradiction between accuracy and computation complexity appropriately (e.g. Google's ‘Search related to’ and Yahoo's ‘Also Try’). In this paper, the recommended words are extracted from the search results of the query, which guarantees the real time of query suggestion properly. A scheme for ranking words based on semantic similarity presents a list of words as the query suggestion results, which ensures the accuracy of query suggestion. Moreover, the experimental results show that the proposed method significantly improves the quality of query suggestion over some popular Web search engines (e.g. Google and Yahoo). Finally, an offline experiment that compares the accuracy of snippets in capturing the number of words in a document is performed, which increases the confidence of the method proposed by the paper. Copyright © 2010 John Wiley & Sons, Ltd.