III-CXT-Small: Information Discovery on Domain Data Graphs

III-CXT-Small：领域数据图上的信息发现

• 批准号: 1216032
• 负责人: Evangelos Christidis
• 金额: $ 15.06万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-31 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216032&HistoricalAwards=false
• 关键词: III; CXT; Small; Information; Discovery

An increasing amount of data is stored in an interconnected manner. Such data range from the Web; hyperlinked pages; to bibliographical data; graph of citations; to biological data; associations between proteins, genes, and publications; to clinical data; associations between patients, hospitalizations, exams and diagnoses. A critical need in order to leverage the available data is the enablement of information discovery, i.e., given a question (query) find pieces of data or associations between them in the data graph that are "good" (relevant, authoritative and specific) for the query, and rank them according to their "goodness". Submitting such queries should not require knowledge of a complex query language (e.g., SQL) or of the details of the data (e.g., schema). Unfortunately, little has been done to provide high-quality information discovery on data graphs in domains other than the Web, where search engines have been successful. This project is expected to have the following broader impacts: (a) Promote participation of FIU (one of the largest Hispanic institutes in the country) minority students in the research process, in the form of independent or senior class projects. (b) Facilitate effective information discovery on biological and clinical data, which can lead to cost savings, and increased research productivity in these domains. The results will be disseminated through publications, public Web demo systems, and the project Web site (http://dblab.cs.ucr.edu/projects/DGID/).

越来越多的数据以互连的方式存储。此类数据来自网络；超链接页面；书目数据；引用图；生物数据；蛋白质、基因和出版物之间的关联；临床数据；患者、住院、检查和诊断之间的关联。为了利用可用数据，一个关键需求是实现信息发现，即，给定一个问题（查询），在数据图中找到“良好”（相关、权威和特定）的数据片段或它们之间的关联。查询，并根据它们的“优点”对它们进行排名。提交此类查询不需要了解复杂的查询语言（例如 SQL）或数据细节（例如模式）。不幸的是，除了搜索引擎在网络上取得成功的领域之外，在数据图上提供高质量信息发现方面几乎没有采取任何措施。该项目预计将产生以下更广泛的影响： (a) 以独立或高级课程项目的形式，促进佛罗里达国际大学（该国最大的西班牙裔学院之一）少数族裔学生参与研究过程。 (b) 促进生物和临床数据的有效信息发现，从而节省成本并提高这些领域的研究生产力。结果将通过出版物、公共网络演示系统和项目网站 (http://dblab.cs.ucr.edu/projects/DGID/) 传播。