Information Extraction from Interconnected Data: An Algorithmic Study

从互连数据中提取信息：算法研究

• 批准号: 283327-2012
• 负责人: Srinivasan, Venkatesh
• 金额: $ 1.6万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525677
• 关键词: Information; Extraction; Interconnected; Data; Algorithmic

Data in today's world is highly complex. It originates from a variety of sources, such as the web and social networks. It is diversely structured and highly dynamic. Previous methods for dealing with data stored as tables no longer apply. There is an urgent need to develop new methods of extracting information from this form of data. The research proposed here will focus on important aspects of information extraction from such data with benefits to areas such as public health (for studying disease patterns) and public policy (for preventing the spread of misinformation).

当今世界的数据非常复杂。它起源于各种来源，如网络和社交网络。它结构灵活，高度动态。以前处理存储为表的数据的方法不再适用。迫切需要开发从这种形式的数据中提取信息的新方法。这里提出的研究将集中在从这些数据中提取信息的重要方面，这些数据对公共卫生（用于研究疾病模式）和公共政策（用于防止错误信息的传播）等领域都有好处。