Extracting action knowledge in security informatics

Actions are the primary way an entity interacts with other entities and acts on the external world. Action knowledge is of vital importance for behavior modeling, analysis and prediction in security informatics. In this paper, we present our approach to action knowledge extraction from Web textual data. Our approach is based on mutual bootstrapping with knowledge reasoning, which can acquire more action knowledge types and require less human participation compared with the related work. We evaluate the performance of our method and demonstrate its effectiveness through experiment.