Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry

Abstract

Life on earth presents elegant solutions to many of the challenges innovators and entrepreneurs across disciplines face every day. To facilitate innovations inspired by nature, there is an emerging need for systems that bring relevant biological information to this application-oriented market. In this paper, we discuss our approach to assembling a system that uses machine learning techniques to assess a scientific article's potential usefulness to innovators, and classifies these articles in a way that helps innovators find information relevant to the challenges they are attempting to solve.