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

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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.