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: 0325464
• 负责人: Amit Sheth
• 金额: $ 80万
• 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: 2003
• 资助国家: 美国
• 起止时间: 2003-10-15 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325464&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Web的关键组成部分，并且已经从平庸走向主流。 第二代“语义网”正在通过添加机器可以理解的语义注释来构建，人类可以从中受益。 对语义Web上的复杂关系进行建模、发现和推理将实现这一愿景，并将文档搜索转变为语义技术支持的更自动化的分析。从搜索到分析的这种转变的开始可以在研究和行业中观察到，因为用户的目光不再局限于基于关键字查找相关文档，而是寻找可采取行动的信息，从而做出决策和获得见解。由于实体识别、自动分类、分类法和本体开发以及元数据提取等方面的进步，大规模的数据语义标注（独立于领域和特定于领域）现在已经成为可能。下一个前沿领域，从根本上改变了我们获取和使用知识的方式，是自动识别这些语义注释数据中实体之间的复杂关系。取代返回包含感兴趣的术语的文档的搜索引擎，将有一个系统向用户或应用程序返回可操作的信息（以及相关的来源和支持证据）。用户通过结合搜索和推理的假设驱动方法与信息世界进行交互，从而实现更复杂的分析和更深入的洞察。该研究将侧重于一个系统的设计，原型设计和评估，称为SemDIS（语义发现），支持索引和查询复杂的语义关系，并由信息信任和出处的概念以及正在调查的假设和论点模型驱动。 这种能力大大提高了情报分析员（及时）获得信息的能力，从而使祖国和世界更加安全。对应于所承担的挑战中涉及的主题的广度和深度，这是一个涉及UGA的LSDIS实验室和UMBC研究人员的合作项目。 SemDIS将产生更广泛的影响，超出研究生的教育和培训以及研究成果的出版。研究结果将与现有和新的课程相结合。现有的体制机制将争取代表性不足群体的学生参与。这项工作还将从研究人员的几个学术-工业合作中获益。 将有机会利用Semagix提供的商业基础设施和原始元数据。研究人员将与工业界合作，并鼓励学生在合作的工业实验室实习。 在更广泛的社会背景下，新兴的以知识为中心的技术引起了合法的隐私和公民自由问题。根据过去的决策经验，研究人员将评论其科学进步的潜在影响。 更多信息可以在http：//www.example.com和http://www.cs.umbc.edu/SemDIS/http://lsdis.cs.uga.edu/SemDIS/