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

KEDS项目正在开发一个科学发现工具包，它指的是从一系列感兴趣的现象的观测数据中识别出新的预测模型。KEDS超越了现有的方法，将背景知识(如变量之间的依赖关系、部分模型以及将使用学习的模型的任务的信息)整合到学习过程中，并提供分析工具来帮助用户理解、评估和比较学习的模型。与当前的工具相比，KEDS为科学发现提供了更灵活、更交互的方法。KEDS的互动技术也被应用到科学教育中，支持一个迭代过程，在这个过程中，学生根据系统提供的个性化、有针对性的反馈来完善他们的心理模型。该项目的影响将是信息技术在科学调查和科学教育中的应用方面的进步。特别是，基兹将支持一种更具互动性的科学发现风格，这将使人类领域的专家能够更有效地将他们之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，互动科学发现的数据集和基准，以及一个文件化的软件包。KEDS系统目前被应用于天文科学领域，但其基本技术在许多其他科学领域具有广泛的适用性，包括地球科学、生物和医学、化学和材料科学。

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.