BUILDING A TIME SERIES ACTION NETWORK FOR EARTHQUAKE DISASTER

The-Minh Nguyen, Takahiro Kawamura, Yasuyuki Tahara, Akihiko Ohsuga

2012

Abstract

Since there is 87% of chance of an approximately 8.0-magnitude earthquake occurring in the Tokai region of Japan within the next 30 years; we are trying to help computers to recommend suitable action patterns for the victims if this massive earthquake happens. For example, the computer will recommend “what should do to go to a safe place”, “how to come back home”, etc. To realize this goal, it is necessary to have a collective intelligence of action patterns, which relate to the earthquake. It is also important to let the computers make a recommendation in time, especially in this kind of emergency situation. This means these action patterns should to be collected in real-time. Additionally, to help the computers understand the meaning of these action patterns, we should build the collective intelligence based on web ontology language (OWL). However, the manual construction of the collective intelligence will take a large cost, and it is difficult in the emergency situation. Therefore, in this paper, we first design a time series action network. We then introduce a novel approach, which can automatically collects the action patterns from Twitter for the action network in realtime. Finally, we propose a novel action-based collaborative filtering, which predicts missing activity data, to complement this action network.

References

  1. Biglobe (2011). http://tr.twipple.jp/info/bunseki/20110427 .html.
  2. Fukazawa, Y. and Ota, J. (2009). Learning user's real world activity model from the web. In IEICE SIG Notes.
  3. Geo (2003). http://www.w3.org/2003/01/geo/wgs84 pos.
  4. Halpin, H., Iannella, R., Suda, B., and Walsh, N. (2010). http://www.w3.org/2006/vcard/ns.
  5. Hugo, L. and Push, S. (2004). a pratical commonsense reasoning toolkit. BT Technology Journal, 22(4).
  6. Kawamura, T., Nguyen, T.-M., and Ohsuga, A. (2009). Building of human activity correlation map from weblogs. In Proc. ICSOFT.
  7. KDDI (2009). Mobile phone based Lifelog.
  8. Koren, Y. (2009). Collaborative filtering with temporal dynamics. In Proc. KDD.
  9. Kudo, T., Yamamoto, K., and Matsumoto, Y. (2004). Applying conditional random fields to japanese morphologiaical analysis. In Proc. EMNLP, pages 230- 237.
  10. Kurashima, T., Fujimura, K., and Okuda, H. (2009). Discovering association rules on experiences from largescale weblogs entries. In Proc. ECIR. LNCS vol 5478. Springer.
  11. Ma, H., King, I., and R-Lyu, M. (2007). Effective missing data prediction for collaborative filtering. In Proc. SIGIR.
  12. Matsuo, Y., Okazaki, N., Izumi, K., Nakamura, Y., Nishimura, T., and Hasida, K. (2007). Inferring longterm user properties based on users' location history. In Proc. IJCAI, pages 2159-2165.
  13. McCallum, A. and Li, W. (2003). Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons.
  14. MIT (2011). http://openmind.media.mit.edu/.
  15. Nakabayashi, I. (2006). Development of urban disaster prevention systems in japan - from the mid-1980s. Journal of Disaster Research, 1.
  16. Nguyen, T.-M., Kawamura, T., Nakagawa, H., Tahara, Y., and Ohsuga, A. (2011). Automatic extraction and evaluation of human activity using conditional random fields and self-supervised learning. Transactions of the Japanese Society for Artificial Intelligence, 26:166-178.
  17. Nikkei (2011). http://e.nikkei.com/e/fr/tnks/Nni20110705D 05HH763.htm.
  18. Nilanjan, B., Dipanjan, C., Koustuv, D., Anupam, J., Sumit, M., Seema, N., Angshu, R., and Sameer, M. (2009). User interests in social media sites: An exploration with micro-blogs. In Proc. CIKM.
  19. NTTDocomo (2009). My Life Assist Service.
  20. Perkowitz, M., Philipose, M., and Donald, K. F. J. (2004). Mining models of human activities from the web. In Proc. WWW.
  21. Poslad, S. (2009). Ubiquitous Computing Smart Devices, Environments and Interactions. Wiley, ISBN: 978-0- 470-03560-3.
  22. Raimond, Y. and Abdallah, http://purl.org/NET/c4dm/timeline.owl.
  23. Shiaokai, W., William, P., Ana-Maria, P., Tanzeem, C., and Matthai, P. (2007). Common sense based joint training of human activity recognizers. In Proc. IJCAI, pages 2237-2242.
  24. Vincent, W., Derek, H., and Qiang, Y. (2009). Crossdomain activity recognition. In Proc. Ubicomp, pages 2159-2165.
  25. W3C (2006). http://www.w3.org/DesignIssues/Notation3.
Download


Paper Citation


in Harvard Style

Nguyen T., Kawamura T., Tahara Y. and Ohsuga A. (2012). BUILDING A TIME SERIES ACTION NETWORK FOR EARTHQUAKE DISASTER . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 100-108. DOI: 10.5220/0003741701000108


in Bibtex Style

@conference{icaart12,
author={The-Minh Nguyen and Takahiro Kawamura and Yasuyuki Tahara and Akihiko Ohsuga},
title={BUILDING A TIME SERIES ACTION NETWORK FOR EARTHQUAKE DISASTER},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={100-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003741701000108},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - BUILDING A TIME SERIES ACTION NETWORK FOR EARTHQUAKE DISASTER
SN - 978-989-8425-95-9
AU - Nguyen T.
AU - Kawamura T.
AU - Tahara Y.
AU - Ohsuga A.
PY - 2012
SP - 100
EP - 108
DO - 10.5220/0003741701000108