Accelerated Discovery in Polymer Materials Domain: Knowledge Extraction and Representation

Acceleration of scientific discovery requires resolution of multiple bottlenecks along the flow connecting the perception of the available data, data analysis and hypothesis generation, and data acquisition via experiments. The challenges of a) ingestion of fast-growing volume of unstructured scientific data, and b) efficient generation of actionable hypotheses come from the limitations of human cognition. We investigate various approaches to the augmentation of the respective capabilities of human subject matter experts in the domain of polymer materials. Transition from curated datasets/databases to scientific knowledge graphs (sKGs) plays central role in this effort. In this talk, I discuss the technical aspects of the construction of sKGs from unstructured data in polymer materials domain, and utilization of sKGs for hypothesis generation, including human-in-the-loop approaches.