System Level Knowledge Analysis and Keyword Extraction in Neuroscience

Abstract

Vast amounts of knowledge resources are emerging from Neuroscience research, thanks to increasingly widely available imaging and analysis technologies and open data sets. Learning, processing and keeping up to date with developments however imply steep learning curves. Advances in Neuroscience research provide insights into the human brain and the mind for brain specialists, but also inform other scientific disciplines such as to complexity science, cognitive computing, medicine in general and general technology and policy sectors. With a few exceptions however, the majority of Neuroscience research outcomes can still be accessed and leveraged mostly only by highly trained neuroscientists and brain informatics researchers in their respective specialisations, as the knowledge and skill sets required to query and manipulate such data is only meaningful for individuals with specific training. This paper identifies and addresses the need to lower the cognitive barriers to accessing Neuroscience research, as it is becoming very relevant to other fields, and to widen its accessibility to a broader range of scholars of other disciplines - such as Computer Science and Information Technology for example, reducing the efforts required in tracking innovation and facilitate knowledge acquisition. Keeping up with the state of the art is a challenge in any field, yet increasing number of researchers, students and practitioners from diverse professions and with a wide range of interests and goals, and multi disciplinary and linguistic backgrounds. The approach proposed here leverages a combination of elementary core methods from semantic technology, including simple corpus and linguistic analysis techniques, and devises a low tech instrument that can be adopted irrespective of the availability of software and level of English language proficiency to acquire the necessary familiarity to handle Neuroscience topics The same method can also be leveraged in other complex knowledge domains. A set of experiments to evaluate the effectiveness of the method is described with preliminary results.