ITR: Knowledge-Enhanced Discovery System (KEDS): Incorporating Background Knowledge for Scientific Discovery

ITR：知识增强发现系统（KEDS）：纳入科学发现的背景知识

• 批准号: 0325329
• 负责人: Marie desJardins
• 金额: $ 77万
• 依托单位: University of Maryland Baltimore County
• 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: 2003-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325329&HistoricalAwards=false
• 关键词: ITR; Knowledge; Enhanced; Discovery; System

The KEDS project is developing a toolkit for scientific discovery, which refers to the identification of new predictive models for a set of phenomena of interest from observational data about those phenomena. KEDS goes beyond existing methods by incorporating background knowledge (such as dependencies between variables, partial models, and information about the task for which the learned models will be used) into the learning process, and by providing analysis tools to aid the user in understanding, evaluating, and comparing the learned models.KEDS provides a more flexible, interactive approach to scientific discovery than current tools. The interactive techniques in KEDS are also being applied to science education, supporting an iterative process in which students refine their mental models, based on personalized, targeted feedback from the system.The impact of the project will be an advance in how information technology is used in scientific investigations and science education. In particular, KEDS will support a more interactive style of scientific discovery, which will allow human domain experts to integrate their previous knowledge into the discovery process more effectively. Expected results include improved interactive methods for scientific discovery and science education, data sets and benchmarks for interactive scientific discovery, and a documented software package. The KEDS system is currently being applied to astronomy science domains, but the underlying techniques have broad applicability to many other science domains, including earth sciences, biology and medicine, chemistry, and materials science.

KEDS项目正在开发一个科学发现工具包，该工具包是指从关于一组感兴趣的现象的观测数据中确定这些现象的新预测模型。 KEDS超越了现有的方法，通过将背景知识（如变量之间的依赖关系，部分模型，以及关于学习模型将用于的任务的信息）纳入学习过程，并通过提供分析工具来帮助用户理解，评估和比较学习模型。 KEDS中的交互技术也被应用于科学教育，支持一个迭代过程，在这个过程中，学生根据系统的个性化、有针对性的反馈来完善他们的心智模型。该项目的影响将是信息技术如何用于科学调查和科学教育的进步。 特别是，KEDS将支持更具交互性的科学发现风格，这将使人类领域专家能够更有效地将他们以前的知识整合到发现过程中。预期成果包括改进科学发现和科学教育的互动方法、互动式科学发现的数据集和基准，以及一个有文件记录的软件包。 KEDS系统目前被应用于天文学科学领域，但其基础技术对许多其他科学领域具有广泛的适用性，包括地球科学，生物学和医学，化学和材料科学。