PFIA 2024
Machine Learning and Knowledge Engineering to enable sensemaking: Case Studies in Robotics and News Analytics
For over a decade my research group in scholarly and news analytics has developed a number of tools and methods which allow us to identify and make sense of the key trends and dynamics characterising academic research and, more recently, news media. Our solutions include (but are not limited to) methods that automatically generate granular ontologies characterising the space of research topics; visual analytics solutions that make sense of the dynamics of research communities; an innovative approach to forecasting the emergence of new research areas; large-scale knowledge graphs to support scientific research; an approach to capture the range of opinions about a topic, as expressed in the media; and others. A key feature of our solutions is the integration of large scale data analytics with knowledge-based analyses and techniques, to improve not just performance but also a system’s explanation capabilities. In this talk I will provide an overview of our research in these areas, presenting both the novel capabilities we have realised and the resulting user-centric deployments.