Extracting Keywords from Research Abstracts for the Neuroinformatics Platform Index Tree

Abstract

Studying the brain as a system requires global collaborations and interdisciplinary approaches which necessitate the development of tools to help scientists in the management, sharing, and synthesis of disparate research resources. Recognizing the benefits and importance of global collaboration and sharing, the INCF (International Neuroinformatics Coordinating Facility) started to coordinate the global effort of establishing different Neuroinformatics (NI) Portals among the participating countries. In Japan, this initiative is starting to take shape through the establishment of the different NI platforms under the coordination of the NIJC (NI Japan Center). Each NI platform in Japan such as "visiome" [http://platform. visiome. org], requires their own set of keywords that represent important terms covering their respective field of study. One important role of this predefined keyword list is to help scientists classify the contents of their contributions and group related resources based on these keywords. It is vital that this predefined list should be properly chosen to cover the necessary areas. Currently, the process of identifying these keywords relies on the availability of human experts which does not scale well considering that the different fields are rapidly evolving. This issue prompted us to develop a new algorithm for a tool to automatically filter terms which are most likely considered as keywords by human experts. We discuss its effectiveness and tested its performance using the abstracts of the Vision Research Journal (VR) as a test case.