Knowledge Sciences & Engineering
A critical response to the flood of data resulting from the information-technology revolution is the need to complement methods that focus on data (e.g., sensor output, database contents, etc.) with those that focus on knowledge in the form of facts, conclusions, and scenarios that are actionable or interpretable by human and computational agents. Exploitation of the latest knowledge-management technologies supporting the engineering of databases and knowledgebases, and the algorithms and systems that allow for their effective, interoperable use by decision-makers, is thus a central goal for this scientific foundation. This is coupled with the development of new approaches to the measurement and analysis of knowledge repositories based on the strongest scientific footing, thus exploiting approaches in mathematical systems theory, network science, computational linguistics, and cognitive science.