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.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics |
| Pages | 368-370 |
| Number of pages | 3 |
| DOIs | |
| State | Published - 2013 |
| Externally published | Yes |
| Event | 11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013 - Seattle, WA, United States Duration: Jun 4 2013 → Jun 7 2013 |
Other
| Other | 11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013 |
|---|---|
| Country | United States |
| City | Seattle, WA |
| Period | 6/4/13 → 6/7/13 |
Fingerprint
Keywords
- action knowledge
- bootstrapping
- knowledge extraction
- reasoning
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
Cite this
Action knowledge extraction from Web text. / Ge, Ansheng; Mao, Wenji; Zeng, Dajun; Wang, Lei.
IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. p. 368-370 6578860.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Action knowledge extraction from Web text
AU - Ge, Ansheng
AU - Mao, Wenji
AU - Zeng, Dajun
AU - Wang, Lei
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - action knowledge
KW - bootstrapping
KW - knowledge extraction
KW - reasoning
UR - http://www.scopus.com/inward/record.url?scp=84883325142&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883325142&partnerID=8YFLogxK
U2 - 10.1109/ISI.2013.6578860
DO - 10.1109/ISI.2013.6578860
M3 - Conference contribution
AN - SCOPUS:84883325142
SN - 9781467362115
SP - 368
EP - 370
BT - IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics
ER -