III: Small: A Theoretical Framework for Practical Entity Resolution in Network Data

III：小：网络数据中实际实体解析的理论框架

• 批准号: 1218488
• 负责人: Lise Getoor
• 金额: $ 50万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218488&HistoricalAwards=false
• 关键词: III; Small; Theoretical; Framework; Practical

In an era of information overload and big data, there is a pressing need to analyze, protect, prioritize and utilize data. Much of this data is inherently relational; thus, it is crucial to understand the benefits, challenges and potential hazards of exploiting the relational properties. Integrating, cleaning, and linking relational data requires matching and resolving references in the data. At the same time, matching and linking pose significant privacy risks. The proposed work develops a theoretical understanding of entity resolution in network data with the goal of developing tools and methods which can tell us how easy or difficult it will be to resolve data in different settings. Making use of the theory, new entity resolution algorithms will be developed with accuracy guarantees and for scaling entity resolution to large-scale data sources. These research results will enable more informed data sharing and usage decisions by individuals, industry, and government. Accurate analysis of network data is of utmost importance to science, medicine and national security. Whether studying socioeconomic trends, integrating data from large microarrays, analyzing organized crime or terrorist networks, or mining financial data for corporate misconduct, accurate network data, and its associated statistics, are crucial. At the same time, understanding how entity resolution effects privacy guarantees, and educating the public about the impact of releasing identifying information, is equally important.

在信息超负荷和大数据的时代，有迫切需要分析，保护，优先级和利用数据。这些数据的大部分是固有的关系。因此，了解利用关系特性的收益，挑战和潜在危害至关重要。集成，清洁和链接关系数据需要数据中的匹配和解决参考。同时，匹配和链接构成了很大的隐私风险。 拟议的工作对网络数据中的实体解决有了理论理解，其目的是开发工具和方法，这些工具和方法可以告诉我们在不同设置中解决数据将是多么容易或困难。 利用理论，将使用准确的保证并将其扩展到大规模数据源来扩展实体分辨率。 这些研究结果将使个人，行业和政府提供更多知情的数据共享和使用决策。 对网络数据的准确分析对于科学，医学和国家安全至关重要。 无论是研究社会经济趋势，整合大型微阵列的数据，分析有组织的犯罪或恐怖网络，还是针对公司不当行为，准确的网络数据及其相关统计数据挖掘财务数据至关重要。同时，了解实体解决方案如何影响隐私保证，并教育公众有关发布识别信息的影响，同样重要。