Action knowledge extraction from Web text

Ansheng Ge, Wenji Mao, Daniel Zeng, Lei Wang

Abstract: 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.

External IDs:dblp:conf/isi/GeMZW13