III: Small: Query and Goal Driven Entity Resolution Framework

III：小型：查询和目标驱动的实体解析框架

• 批准号: 1118114
• 负责人: Sharad Mehrotra
• 金额: $ 50万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118114&HistoricalAwards=false
• 关键词: III; Small; Query; Goal; Driven

Data cleaning technologies, traditionally designed to improve quality of data in back-end data warehouses, are fast emerging as a vital component of real-time information access. As the Web evolves towards supporting interactive analytics and basic search migrates from simple keyword retrieval to retrieval based on semantically richer concepts (e.g., entities) extracted from web pages, the need for "on-the-fly" cleaning techniques that can help alleviate data quality challenges is rapidly increasing. This project explores three new innovations that will help advance data cleaning towards becoming an embedded enabling technology for real-time information access. The first innovation is "query-aware data cleaning" which is based on the observation that the specificity of the real-time task such as a query can be exploited significantly to bring new optimizations to the data cleaning process. The second innovation is a data cleaning framework that migrates from the "best-effort" adhoc setup of today's systems into a principled approach that exposes and exploits a fundamental tradeoff between the cost of cleaning and quality of results achieved. Finally, since results of cleaning need to be fed to the end-user or analysis code, the proposal postulates and addresses approaches towards how results processed through data cleaning code can be presented to the end-recipient. The primary contribution is mechanisms to hide the uncertainty in the data and determinize the results while maximizing the end application goals. The proposed research is intended to bring transformative improvements in interactive analytics and search on the web by facilitating real-time data cleaning and data quality enhancements. The project also aims to benefit the research community by incorporating mechanisms developed as part of this research into the Web People Search Technology (WEST), enabling WEST to become a real-time on-the-fly web people search tool. The goal is to support WEST as a plug-and-play system wherein other researchers could embed and test their data cleaning algorithms and tools. Finally, the planned research, system development, and educational activities are going to significantly enhance the educational experience of students, preparing them for a brighter future in the today's knowledge driven society.For further information see the project web site at the URL: http://sherlock.ics.uci.edu

传统上用于提高后端数据仓库中数据质量的数据清洗技术正迅速成为实时信息访问的重要组成部分。随着Web朝着支持交互式分析和基本搜索的方向发展，从简单的关键字检索迁移到基于语义上更丰富的概念（例如，实体），对能够帮助减轻数据质量挑战的“即时”清理技术的需求正在迅速增加。该项目探索了三项新的创新，这些创新将有助于推进数据清洗，使其成为实时信息访问的嵌入式支持技术。第一个创新是“查询感知数据清理”，它基于这样的观察，即可以显著地利用诸如查询之类的实时任务的特异性来为数据清理过程带来新的优化。第二个创新是一个数据清理框架，它从当今系统的“尽力而为”临时设置迁移到一个原则性的方法，该方法暴露并利用了清理成本和所获得结果质量之间的基本权衡。最后，由于清洗结果需要提供给最终用户或分析代码，该提案假定并解决了如何将通过数据清洗代码处理的结果呈现给最终接收者的方法。主要贡献是隐藏数据中的不确定性并确定结果的机制，同时最大限度地提高最终应用程序的目标。拟议的研究旨在通过促进实时数据清理和数据质量增强，为网络上的交互式分析和搜索带来变革性的改进。该项目还旨在通过将作为本研究一部分开发的机制整合到网络人物搜索技术（WEST）中，使WEST成为实时动态网络人物搜索工具，从而使研究界受益。我们的目标是支持WEST作为一个即插即用的系统，其他研究人员可以嵌入和测试他们的数据清理算法和工具。最后，计划中的研究、系统开发和教育活动将显著增强学生的教育体验，为他们在当今知识驱动的社会中创造更光明的未来做好准备。http://sherlock.ics.uci.edu