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

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。航空安全等对国家安全至关重要的应用对当前的信息技术构成了重大挑战，并为进一步研究开发下一代IT解决方案提供了良好的来源。近年来，在开发新一代语义技术方面，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）等技术的应用取得了重大进展。 语义技术有助于将数据的含义联系起来，更有意义地组织数据，有意义地关联数据，以及将数据转换为信息，以便更有效地做出决策，并找到与用户需求相关的信息。 它们有助于语法和表示以及语义的互操作性。语义网是Web的下一个发展阶段，它的前景令人兴奋，这一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一种称为语义内容组织和检索引擎（SCORE）的语义技术的发展。使用SCORE的能力，快速创建本体驱动的代理，而无需编程，它已经有可能（a）快速创建和维护大型知识库（例如每个域超过一百万个实体和关系）基于多个半结构化和结构化的知识源，（但不是完全）自动化的方式，以及（B）从静态和动态的非结构化（文本）、半结构化和结构化源（例如，查询驱动的）内容。这一技术也已商业化，目前正用于航空安全和情报应用。虽然这些应用程序的细节不能讨论，由于政府和机构的规定，许多技术上可能的能力还没有通过政策的考虑，我们想象一个原型应用程序的国土安全利益，帮助识别和筛选乘客的安全风险，以制定相关的IT研究的要求。这种应用提出的两个重要挑战包括（a）快速识别涉及实体的语义关联（例如航班上的一名乘客或一组乘客），以及（B）识别感兴趣语义关联的知识发现（例如可能构成风险的）。我们的目标是研究新的技术和提高技术的有效性，以确定语义关联和知识发现，通过利用庞大的知识库。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化建模和高性能计算，包括基于本体的上下文处理和感兴趣关系的相关性排名。我们的方法涉及引导早期的语义元数据提取，多本体查询处理和其他工具，从正在进行的InfoQuilt项目，使我们可以创建知识库和元数据从公开的来源，使有意义的技术评估的研究。

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.