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

在网络上和网络之外的可用文本嵌入了前所未有的有价值的结构化数据，“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流行查询范例，但对于需要嵌入文本的结构化数据的复杂信息需求来说，它的表达能力往往不足。为了满足这样的需求，用户（例如，流行病学家根据媒体报道，对一个偏远国家最近爆发的食源性疾病进行统计）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，直到他们找到适当的（结构化的）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）来实现结构化查询。为了捕获不同的用户信息需求，并摆脱“一刀切”的查询方法，该方法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式：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，以便撰写一份关于发展中国家最近沙门氏菌爆发的报告）正在寻找一些探索性的结果，这些结果应该很快就会回来；有些时候，使用者（例如上述调查食源性疾病的流行病学家）需要全面的结果，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专门的基于成本的查询优化器，考虑到查询执行计划的效率和关键的结果质量。所产生的技术将帮助各种各样的用户和信息需求，通过实现与文本数据库的高效、多样的交互——用于复杂的搜索和数据挖掘——这在今天的技术中是繁琐的或不可能的。该项目的研究和教育部分将依赖并鼓励三个互补的计算机科学学科的紧密整合，即自然语言处理、信息检索和数据库。该项目还将通过Web （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。

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/).