Action knowledge is an important type of behavioral knowledge and of vital importance to many applications in social computing, especially in behavior modeling, analysis and prediction. In this paper, we present a computational method to action knowledge extraction from online media. Our approach is based on mutual bootstrapping and combined with knowledge reasoning. Compared with the related work, our approach can acquire more types of action knowledge, and needs much less human labor. We evaluate the performance of our method using the Web textual data from security informatics domain. The experimental results show the effectiveness of our proposed method.

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.

Inspired by the psychological attribution theory, this article presents a computational approach to construct causal scenarios and facilitate social causality studies based on online textual data.

Open source intelligence is becoming more and more important in security-related applications. Effective extraction of valuable information from these intelligence sources so as to understand the content of intelligence is one of the central issues in security domain. To address this key problem, this paper studies how to extract domain events and generate story representation of the events. We implement a story extraction platform (SEP) which applies pattern matching to extract events from Web news and organizes events based on theme using narrative structure. We also design extraction rules, use domain-specific features and employ ontology to facilitate story extraction in SEP. The experimental results show the effectiveness of our system in security informatics.