ITR: Semantic Association Identification and Knowledge Discovery for National Security Applications (IDM Program)

ITR：国家安全应用的语义关联识别和知识发现（IDM 计划）

• 批准号: 0219649
• 负责人: Amit Sheth
• 金额: $ 20万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0219649&HistoricalAwards=false
• 关键词: ITR; Semantic; Association; Identification; Knowledge

Role of information technology (IT) is recognized to be a critical component in the effort of improving national security, including homeland defense. Applications of importance to national security, such as aviation security, pose significant challenges to current information technology and provide excellent source for further research in developing next generation IT solutions. Recently, there is significant advance in applying techniques from database and information systems, knowledge representation, AI, information retrieval including text categorization, lexical and language analysis and others in developing a new generation of semantic technologies. Semantic technologies help in associating meaning of data and in more meaningfully organizing data, in meaningfully correlating data, as well as in converting data into information for more effective decision making and in finding information that contextually relevant to users' needs. They help with syntactic and representational as well as semantic interoperability. This general area of research is also getting renewed attention now that there is considerable excitement in the vision of the Semantic Web, characterized as the next phase of the Web.Results from several of our past and continuing research projects have led to the development a semantic technology called Semantic Content Organization and Retrieval Engine (SCORE). Using SCORE's ability to quickly create ontology-driven agents without programming, it has been possible to (a) quickly create and maintain large knowledge bases (such as over one million entities and relationships per domain) base from multiple semi structured and structured sources of knowledge in largely (but not fully) automated ways, and (b) ability to create semantic (domain specific) metadata from unstructured (text), semi structured and structured sources of static and dynamic (e.g., query driven) content. This technology has also been commercialized and is being used in aviation security and intelligence applications. While specifics of these applications cannot be discussed due to government and agency regulations, and many technologically possible capabilities have yet to pass through policy considerations, we imagine a prototype application of homeland security interest that help in identifying and screening a passenger with respect to security risk to develop requirements for relevant IT research. Two important challenges posed by such an application include (a) rapid identification of semantic associations involving entities (such as a passenger or a group of passengers on a flight), and (b) knowledge discovery that identify semantic associations of interest (such as those that may pose a risk).Our goal is to research new techniques and improving effectiveness of techniques to identify semantic associations and knowledge discovery by exploiting a large knowledge base. Specific objectives include (a) ontology driven lazy semantic metadata extraction (i.e., annotation) to complement traditional active metadata extraction techniques, and (c) formal modeling and high-performance computation of semantic association discovery including ontology-based contextual processing and relevancy ranking of interesting relationships. Our approach involves bootstrapping earlier research on semantic metadata extraction, multi-ontology query processing and other tools from on-going InfoQuilt project so that we can create knowledge bases and metadata from publicly available sources to enable meaningful evaluation of the techniques.

信息技术 (IT) 的作用被认为是改善国家安全（包括国土防御）的关键组成部分。对国家安全具有重要意义的应用，例如航空安全，对当前的信息技术提出了重大挑战，并为开发下一代信息技术解决方案的进一步研究提供了良好的资源。近年来，数据库和信息系统、知识表示、人工智能、信息检索（包括文本分类）、词汇和语言分析等技术在开发新一代语义技术方面取得了重大进展。 语义技术有助于关联数据的含义、更有意义地组织数据、有意义地关联数据、将数据转换为信息以进行更有效的决策以及查找与用户需求上下文相关的信息。 它们有助于句法、表征以及语义的互操作性。由于语义网（被称为网络的下一阶段）的愿景令人兴奋，因此这一一般研究领域也重新受到关注。我们过去和正在进行的几个研究项目的结果导致了语义网的发展。称为语义内容组织和检索引擎（SCORE）的技术。利用 SCORE 无需编程即可快速创建本体驱动代理的能力，可以 (a) 从多个半结构化和结构化知识源快速创建和维护大型知识库（例如每个领域超过一百万个实体和关系）以很大程度上（但不是完全）自动化的方式，以及（b）从静态和动态（例如查询驱动）内容的非结构化（文本）、半结构化和结构化源创建语义（特定领域）元数据的能力。这项技术也已经商业化，并被用于航空安全和情报应用。虽然由于政府和机构的规定，这些应用程序的具体细节无法讨论，而且许多技术上可能的功能尚未经过政策考虑，但我们想象一个国土安全利益的原型应用程序，有助于识别和筛选乘客的安全风险制定相关信息技术研究的要求。这种应用程序带来的两个重要挑战包括（a）快速识别涉及实体（例如航班上的一名乘客或一群乘客）的语义关联，以及（b）识别感兴趣的语义关联（例如那些这可能会带来风险）。我们的目标是研究新技术并提高技术的有效性，以通过利用大型知识库来识别语义关联和知识发现。具体目标包括（a）本体驱动的惰性语义元数据提取（即注释），以补充传统的主动元数据提取技术，以及（c）语义关联发现的形式建模和高性能计算，包括基于本体的上下文处理和相关性排序有趣的关系。我们的方法涉及引导早期对语义元数据提取、多本体查询处理和正在进行的 InfoQuilt 项目中的其他工具的研究，以便我们可以从公开可用的来源创建知识库和元数据，从而对技术进行有意义的评估。