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包括国土防御在内的国家安全的努力的关键组成部分。航空安全等对国家安全具有重要意义的应用对当前的信息技术提出了重大挑战，并为进一步研究开发下一代信息技术解决方案提供了极好的资源。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)等技术都取得了重大进展。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，以及将数据转换为信息，以便更有效地做出决策，并找到与用户需求上下文相关的信息。它们有助于语法和表示以及语义的互操作性。随着被描述为下一阶段网络的语义网的愿景中有相当大的兴奋，这一总体研究领域也得到了新的关注。我们过去和继续的几个研究项目的结果导致了一种名为语义内容组织和检索引擎(SCORE)的语义技术的开发。使用SCORE无需编程即可快速创建本体驱动的代理的能力，已经可以(A)以主要(但不是完全)自动化的方式从多个半结构化和结构化的知识来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)、半结构化和结构化来源创建语义(领域特定的)元数据的能力。这项技术也已商业化，并正在用于航空安全和情报应用。虽然由于政府和机构的规定，这些应用的细节无法讨论，而且许多技术上可能的能力尚未通过政策考虑，但我们设想一个国土安全利益的原型应用，帮助识别和筛查乘客的安全风险，以制定相关IT研究的要求。这类应用带来的两个重要挑战包括：(A)快速识别涉及实体(如航班上的一名或一组乘客)的语义关联；(B)识别感兴趣的语义关联(如可能构成风险的语义关联)的知识发现。我们的目标是研究新技术，并通过利用大型知识库来提高识别语义关联和知识发现的技术的有效性。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兴趣关系的相关性排名。我们的方法包括从正在进行的InfoQuilt项目中引导早期关于语义元数据提取、多本体查询处理和其他工具的研究，以便我们可以从公开可用的来源创建知识库和元数据，以便能够对技术进行有意义的评估。

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.