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.

在信息过载和大数据时代，迫切需要分析、保护、优先考虑和利用数据。这些数据中的大部分本质上是相关的;因此，了解利用关系属性的好处、挑战和潜在危险至关重要。集成、清理和链接关系数据需要匹配和解析数据中的引用。与此同时，匹配和链接会带来重大的隐私风险。 拟议的工作发展的网络数据中的实体解析的理论理解，开发工具和方法，可以告诉我们如何容易或困难，这将是解决数据在不同的设置的目标。 利用该理论，将开发新的实体解析算法，具有精度保证，并将实体解析扩展到大规模数据源。 这些研究成果将使个人、行业和政府能够更明智地共享数据和做出使用决策。 准确分析网络数据对科学、医学和国家安全至关重要。 无论是研究社会经济趋势，整合来自大型微阵列的数据，分析有组织犯罪或恐怖主义网络，还是挖掘企业不当行为的财务数据，准确的网络数据及其相关统计数据都至关重要。与此同时，了解实体解析如何影响隐私保障，并教育公众了解发布身份信息的影响，也同样重要。