@Article{信息:doi 10.2196 / / jmir.5.4。e31，作者="Bader, Judith L and Theofanos, Mary Frances"，标题="在互联网上搜索癌症信息:分析自然语言搜索查询"，期刊="J Med Internet Res"，年="2003"，月=" 12 "，日="11"，卷="5"，数="4"，页="e31"，关键词="癌症;互联网;搜索引擎;背景:搜索健康信息是互联网用户最常见的任务之一。许多用户开始在流行的搜索引擎上搜索，而不是在著名的健康信息网站上搜索。我们知道，许多访问我们(国家癌症研究所)网站Cancer .gov的人是通过搜索引擎结果中的链接到达的。目的:为了更多地了解普通公众用户的特定需求，我们想了解普通用户真正想知道的关于癌症的信息，他们是如何提问的，以及他们使用了多少细节。方法:美国国家癌症研究所与AskJeeves公司合作开发了一种方法，用于捕获、取样和分析Ask.com网站上3个月来与癌症相关的查询。Ask.com是一个著名的美国消费者搜索引擎，每周收到超过3500万次查询。AskJeeves使用由国家癌症研究所提供的500个术语和词根组成的基准集，在2001年8月确定了一个为期一周的癌症查询测试样本。 From these 500 terms only 37 appeared ≥ 5 times/day over the trial test week in 17208 queries. Using these 37 terms, 204165 instances of cancer queries were found in the Ask.com query logs for the actual test period of June-August 2001. Of these, 7500 individual user questions were randomly selected for detailed analysis and assigned to appropriate categories. The exact language of sample queries is presented. Results: Considering multiples of the same questions, the sample of 7500 individual user queries represented 76077 queries (37{\%} of the total 3-month pool). Overall 78.37{\%} of sampled Cancer queries asked about 14 specific cancer types. Within each cancer type, queries were sorted into appropriate subcategories including at least the following: General Information, Symptoms, Diagnosis and Testing, Treatment, Statistics, Definition, and Cause/Risk/Link. The most-common specific cancer types mentioned in queries were Digestive/Gastrointestinal/Bowel (15.0{\%}), Breast (11.7{\%}), Skin (11.3{\%}), and Genitourinary (10.5{\%}). Additional subcategories of queries about specific cancer types varied, depending on user input. Queries that were not specific to a cancer type were also tracked and categorized. Conclusions: Natural-language searching affords users the opportunity to fully express their information needs and can aid users na{\"i}ve to the content and vocabulary. The specific queries analyzed for this study reflect news and research studies reported during the study dates and would surely change with different study dates. Analyzing queries from search engines represents one way of knowing what kinds of content to provide to users of a given Web site. Users ask questions using whole sentences and keywords, often misspelling words. Providing the option for natural-language searching does not obviate the need for good information architecture, usability engineering, and user testing in order to optimize user experience. ", issn="1438-8871", doi="10.2196/jmir.5.4.e31", url="//www.mybigtv.com/2003/4/e31/", url="https://doi.org/10.2196/jmir.5.4.e31", url="http://www.ncbi.nlm.nih.gov/pubmed/14713659" }