Collaborative Proposal: ITR-SemDIS: Discovering Complex Relationships in the Semantic Web

合作提案：ITR-SemDIS：发现语义网中的复杂关系

• 批准号: 0714441
• 负责人: Amit Sheth
• 金额: --
• 依托单位: Wright State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0714441&HistoricalAwards=false
• 关键词: Collaborative; Proposal; ITR; SemDIS; Discovering

Research in search techniques was a critical component of the first generation of the Web, and has gone from academe to mainstream. A second generation "Semantic Web" is being built by adding semantic annotations that machines can understand and from which humans can benefit. Modeling, discovering and reasoning about complex relationships on the Semantic Web will enable this vision and transform the hunt for documents into a more automated analysis enabled by semantic technology. The beginnings of this shift from search to analysis can be observed in research and industry as users look beyond finding relevant documents based on keywords to finding actionable information leading to decision making and insights. Large scale semantic annotation of data (both domain-independent and domain-specific) is now possible because of an accumulation of advances in entity identification, automatic classification, taxonomy and ontology development, and metadata extraction. The next frontier, which fundamentally changes the way we acquire and use knowledge, is to automatically identify complex relationships between entities in this semantically annotated data. Instead of a search engine that returns documents containing terms of interest, there will be a system that returns actionable information (with the associated sources and supporting evidence) to a user or application. The user interacts with information universe through a hypothesis driven approach that combines search and inferencing, enabling more complex analysis and deeper insight. The research will focus on the design, prototyping and evaluation of a system, called SemDIS (Semantic Discovery) that supports indexing and querying of complex semantic relationships and is driven by notions of information trust and provenance and models of hypotheses and arguments under investigation. Such a capability greatly enhances the capacity of intelligence analysts to obtain (in time) information leading to a more secure homeland and world. Corresponding to the breadth and depth of the topics involved in the challenge undertaken, this is a collaborative project involving researchers at UGA's LSDIS lab and UMBC. SemDIS will have broader impacts beyond the education and training of graduate students, and the publication of research findings. Results from the research will be integrated with courses, both existing and new. Institutional mechanisms in place will seek participation of students from underrepresented groups. The work will also gain from several academic-industry collaborations of the investigators. There will be an opportunity to leverage commercial infrastructure and raw metadata provided by Semagix. The researchers will collaborate with industry, and the students will be encouraged to intern at collaborating industrial labs. Within a broader social context, emerging knowledge-centric technologies raise legitimate privacy and civil liberties concerns. Building upon past policy making experience, the investigators will comment on potential implications of their scientific progress. More information can be found at http://http://lsdis.cs.uga.edu/SemDIS/ and at http://www.cs.umbc.edu/SemDIS/

搜索技术的研究是第一代网络的关键组成部分，并且已经从学术界转变为主流。 通过添加机器可以理解并从中受益的语义注释来构建第二代“语义网”。 关于语义网络上复杂关系的建模，发现和推理将使这一愿景能够将文档的狩猎转变为通过语义技术实现的更自动化的分析。从搜索到分析转变的开始，可以在研究和行业中观察到，因为用户不仅可以根据关键字找到相关文档来找到可行的信息，从而找到了可行的信息，从而导致决策和见解。由于实体识别，自动分类，分类学和本体论开发以及元数据提取方面的进步，数据的大规模语义注释（与域无关和特定于域特异性）现在是可能的。从根本上改变我们获取和使用知识的方式的下一个边界是自动确定该语义注释数据中实体之间的复杂关系。与其将包含感兴趣条款的文档返回文档的搜索引擎，还将有一个系统将可行的信息（带有关联的来源和支持证据）返回给用户或应用程序。用户通过假设驱动的方法与信息宇宙进行交互，该方法结合了搜索和推断，从而实现了更复杂的分析和更深入的见解。这项研究将重点关注一个系统的设计，原型制作和评估，称为Semdis（语义发现），该系统支持和查询复杂的语义关系，并由信息信任和出处的概念以及所研究的假设和论点的概念驱动。 这样的能力大大提高了情报分析师获得（及时）信息的能力，从而导致更安全的家园和世界。与挑战所涉及的主题的广度和深度相对应，这是一个合作项目，涉及UGA的LSDIS LAB和UMBC的研究人员。 Semdis将在研究生的教育和培训以及研究结果的出版之外产生更广泛的影响。研究结果将与现有和新课程集成。制度机制将寻求来自代表性不足的群体的学生的参与。这项工作还将从研究人员的几个学术行业合作中获得。 将有机会利用Semagix提供的商业基础设施和原始元数据。研究人员将与行业合作，并鼓励学生在协作工业实验室实习。 在更广泛的社会背景下，新兴的以知识为中心的技术提高了合法的隐私和公民自由的关注。在过去的政策制定经验的基础上，调查人员将评论其科学进步的潜在影响。 更多信息可以在http：// http：//lsdis.cs.uga.edu/semdis/以及http://www.cs.umbc.edu/semdis/上找到。