III-COR-Small: Beyond Keyword Search: Enabling Diverse Structured Query Paradigms over Text Databases

III-COR-Small：超越关键字搜索：在文本数据库上启用多样化的结构化查询范式

• 批准号: 0811038
• 负责人: Luis Gravano
• 金额: $ 44.9万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811038&HistoricalAwards=false
• 关键词: III; COR; Small; Beyond; Keyword

The text available on the Web and beyond embeds unprecedented volumesof valuable structured data, "hidden" in natural language. Forexample, a news article might discuss an outbreak of an infectiousdisease, reporting the name of the disease, the number of peopleaffected, and the geographical regions involved. Keyword search, theprevalent query paradigm for text, is often insufficiently expressivefor complex information needs that require structured data embedded intext. For such needs, users (e.g., an epidemiologist compilingstatistics, as reported in the media, on recent foodborne diseaseoutbreaks in a remote country) are forced to embark in labor-intensivecycles of keyword-based document retrieval and manual documentfiltering, until they locate the appropriate (structured) information.To move beyond keyword search, this project exploits informationextraction technology, which identifies structured data in text, toenable structured querying. To capture diverse user information needsand depart from a "one-size-fits-all" querying approach, which isinappropriate for this extraction-based scenario, this projectexplores a wealth of structured query paradigms: sometimes users(e.g., a high-school student in need of some quick examples andstatistics for a report on recent salmonella outbreaks in developingcountries) are after a few exploratory results, which should bereturned fast; some other times, users (e.g., the above epidemiologistinvestigating foodborne diseases) are after comprehensive results, forwhich waiting a longer time is acceptable. The project developsspecialized cost-based query optimizers for each query paradigm,accounting for the efficiency and, critically, the result quality ofthe query execution plans. The technology produced will assist a vastrange of users and information needs, by enabling efficient, diverseinteractions with text databases -- for sophisticated searching anddata mining -- that are cumbersome or impossible with today'stechnology. The research and educational components of the projectwill rely on -- and encourage -- a tight integration of threecomplementary Computer Science disciplines, namely, natural languageprocessing, information retrieval, and databases. The project willalso provide data sets and source code, for experimentation andevaluation, to the community at large over the Web (http://extraction.cs.columbia.edu/).

网络上的文本和其他文本都嵌入了前所未有的大量有价值的结构化数据，这些数据“隐藏”在自然语言中。例如，一篇新闻文章可能会讨论一种传染病的爆发，报道疾病的名称、受影响的人数和涉及的地理区域。 关键字搜索是文本的普遍查询范式，但对于需要结构化数据嵌入文本的复杂信息需求来说，它往往表现力不足。对于这些需求，用户（例如，正如媒体报道的那样，一位流行病学家正在编制有关最近偏远国家食源性疾病爆发的统计数据）被迫进入基于关键字的文档检索和手动文档过滤的劳动密集型周期，直到找到适当的（结构化）信息。为了超越关键字搜索，该项目利用信息提取技术，识别文本中的结构化数据，以实现结构化查询。为了捕获不同的用户信息需求，并摆脱“一刀切”的查询方法，这是不适合这种基于提取的场景，该项目探索了丰富的结构化查询范例：有时用户（例如，一名高中生需要一些快速的例子和统计数据来报告最近在发展中国家爆发的沙门氏菌）是在一些探索性的结果之后，这些结果应该被快速返回;其他时候，用户（例如，上述流行病学家（负责调查食源性疾病的专家）正在等待全面的结果，因此等待较长时间是可以接受的。 该项目为每个查询范例开发了专门的基于成本的查询优化器，考虑了查询执行计划的效率和关键的结果质量。 所产生的技术将有助于大量的用户和信息需求，通过实现与文本数据库的有效，多样化的交互-用于复杂的搜索和数据挖掘-这在今天的技术中是繁琐或不可能的。 该项目的研究和教育部分依赖于-并鼓励-三个互补的计算机科学学科，即自然语言处理，信息检索和数据库的紧密结合。该项目还将通过网络（http：//extraction.cs.columbia.edu/）向整个社区提供数据集和源代码，用于实验和评估。