Effectiveness of Automatic and Semi-Automatic Methods to Collect Common Sense Knowledge

Abstract

Common sense knowledge (CSK) comes naturally to humans, but is very hard for computers to comprehend. However it is critical for machines to behave intelligently, and as such collecting CSK has become a prevalent field of research. Whilst a lot of research has been done to develop CSK acquisition methods, not much work has been done to survey the literature that already exists. Furthermore the surveys that have been done are outdated, and as such there is a clear gap in the literature. This paper will survey the different approaches to CSK acquisition and evaluate their effectiveness, as a way of gauging their real life applicability. It will also compare the current \textit{state of the art} methods, to some previous work to illustrate the progress that has been made and project that into the future. Furthermore this paper will also create a taxonomy categorizing the surveyed literature in order give a better overview of existing methods. Finally, from the literature surveyed it is clear that these methods have made a lot of progress, but aren't quite yet at the same level as human performance. Nevertheless they have become robust enough to be deployed in real applications.