III-COR - Small: Searchable and Shareable Visually Observed Knowledge Base

III-COR - 小：可搜索和可共享的视觉观察知识库

• 批准号: 0812515
• 负责人: Chi-Ren Shyu
• 金额: $ 44.93万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812515&HistoricalAwards=false
• 关键词: III; COR; Small; Searchable; Shareable

In fields where visually based tacit knowledge is central to decision-making, such as image analysis within the medical or intelligence communities, the difficulty of transferring hard-won expertise to novices is compounded by a lack of sufficient knowledge transfer tools. Static media and current computational approaches are not up to the task of capturing highly complex visually based tacit knowledge. A visual knowledge infrastructure based on enhanced computational approaches and robust tools must be developed that can harness the extant wealth of visual-knowledge expertiseand will facilitate the rapid and effective transfer of that knowledge to and use by learners. Five necessary computer science research goals must be reached in order to build such a visual knowledge infrastructure. They include the development of a declarative knowledge module for visual and textual content management, visual mining algorithms for discovering associations in a multi-dimensional feature space, a cognitive model for visual media analysts, a case-based reasoning system for knowledge repositories and acquisition, and a digital library with a robust knowledge exchange framework for interdisciplinary communities. The cognitive model of visual media analysts proposed in the project has the ability to index facts and production rules from associations among low-level visual features and high-level semantics. To make the machine-readable visual knowledge accessible and sharable by novices, the proposed case-based reasoning system will provide the functions for knowledge retrieval. An efficient visual-knowledge infrastructure is necessary to meet the needs of high quality and cost-effective basic science research and health care. Other impacts include joint mentoring of interdisciplinary students, and the creation of digital libraries for curriculum design in multidisciplinary training, as well as the engagement of underrepresented high school students and undergraduates in the research.

在以视觉为基础的隐性知识对决策至关重要的领域，例如医疗或情报界的图像分析，由于缺乏足够的知识转移工具，将来之不易的专业知识转移给新手的难度更大。静态媒体和当前的计算方法无法完成捕获高度复杂的基于视觉的隐性知识的任务。必须开发基于增强计算方法和强大工具的视觉知识基础设施，以利用现有的视觉知识专业知识财富，并促进学习者快速有效地将这些知识转移和使用。为了建立这样一个可视化的知识基础设施，必须达到五个必要的计算机科学研究目标。它们包括开发用于视觉和文本内容管理的声明性知识模块，用于发现多维特征空间中的关联的视觉挖掘算法，用于视觉媒体分析师的认知模型，用于知识库和获取的基于案例的推理系统，以及用于跨学科社区的具有强大知识交换框架的数字图书馆。本项目提出的视觉媒体分析师认知模型能够从低层次视觉特征和高层次语义之间的关联中对事实和生产规则进行索引。为了使机器可读的视觉知识能够被新手访问和共享，本文提出的基于案例的推理系统将提供知识检索功能。高效的视觉知识基础设施是满足高质量、高成本效益的基础科学研究和卫生保健需求的必要条件。其他影响包括对跨学科学生的联合指导，为多学科培训课程设计创建数字图书馆，以及让代表性不足的高中生和本科生参与研究。