ITR: Unapparent Information Revelation - Creation, Visualization and Mining of Concept Chain Graphs

ITR：隐性信息揭示——概念链图的创建、可视化和挖掘

• 批准号: 0325404
• 负责人: Rohini Srihari
• 金额: --
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325404&HistoricalAwards=false
• 关键词: ITR; Unapparent; Information; Revelation; Creation

There are potentially valuable nuggets of information hidden in document collections generated by multiple authors, working independently at various times. Such information is not explicit, but can be inferred by following chains of concepts and associations. Users surfing the web may need to be monitored with the goal of deriving their true information need, which could be motivated by malicious intent. The problem of unintended information revelation (UIR) is a special case of text mining where the documents represent some pre-selected subset of interest to a user, generated through purposeful querying or surfing. The goal is to quantify the information revealed by this subset and to detect significant chains of concepts and associations. This effort focuses on the development of a UIR framework and toolkit that covers the following areas: (i) probabilistic frameworks for concept chain graphs (CCG): a new information representation conducive to text mining; (ii) automatic construction of CCGs from representative document collections using pre-existing ontologies and machine learning techniques in information extraction; (iii) discovery tools that quantify information revealed and reveal hidden, information rich paths within the CCG, and (iv) interactive visualization tools for the CCG. This new framework facilitates better visualization and analysis of information than existing information retrieval (IR) representations. This project should impact several applications, most notably homeland defense applications. The UIR toolkit has the potential to expose sensitive information available on unclassified websites. It can also be used to ascertain whether that information is benign or safe to disseminate. Applications in discovery from scientific documents are also enabled.

在多个作者在不同时间独立工作生成的文档集合中隐藏着潜在的有价值的信息。 这些信息并不明确，但可以通过以下概念和关联链推断出来。 上网的用户可能需要被监控，目的是获取他们真正的信息需求，这可能是出于恶意。 非预期信息泄露（UIR）问题是文本挖掘的一种特殊情况，其中文档表示用户通过有目的的查询或浏览生成的一些预先选择的感兴趣的子集。 我们的目标是量化这个子集所揭示的信息，并检测重要的概念和关联链。这项工作的重点是开发一个统一信息检索框架和工具包，涵盖以下领域：㈠概念链图的概率框架：一种有助于文本挖掘的新的信息表示法; ㈡利用信息提取中已有的本体和机器学习技术，从代表性文件集自动构建概念链图;（iii）量化所揭示的信息并揭示CCG内隐藏的信息丰富路径的发现工具，以及（iv）用于CCG的交互式可视化工具。这个新的框架比现有的信息检索（IR）表示更好地促进信息的可视化和分析。 该项目将影响多个应用程序，尤其是国土防御应用程序。 UIR工具包有可能暴露非机密网站上的敏感信息。 它还可以用来确定该信息是否是良性或安全的传播。还可以从科学文献中发现应用程序。