The aim of SciKnow 2017 is to bring together researchers interested in representing and capturing knowledge about science so that it can be used by intelligent systems to support scientific research and discovery. From the early days of Artificial Intelligence, researchers have been interested in capturing scientific knowledge to develop intelligent systems. There are a variety of formalisms used today in different areas of science. Ontologies are widely used for organizing knowledge, particularly in biology and medicine. Process representations are used to do qualitative reasoning in areas such as physics and chemistry. Probabilistic graphical models are used by machine learning researchers, e.g., in climate modeling.

In addition to enabling more advanced capabilities for intelligent systems in science, capturing scientific knowledge enables knowledge dissemination and open science practices. This is increasingly more important to enable the reuse of scientific knowledge across scientific disciplines, businesses and the public. Although great advances have been made, scientific knowledge is complex and poses great challenges for knowledge capture. This workshop will provide a forum to discuss existing forms of scientific knowledge representation and existing systems that use them, and to envision major areas to augment and expand this important field of research. The increasing emphasis in open science has had a major focus on data sharing but it needs to encompass knowledge as well. There are many research challenges in open sharing and reuse of scientific knowledge that need to be addressed in future research.