Nothing Special   »   [go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Rescue decision via Earthquake Disaster Knowledge Graph reasoning

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

In earthquake disaster scenes, the primary task is to formulate corresponding rescue strategies accurately and quickly according to the state and material of buildings. Therefore, researchers have researched earthquake rescue decisions based on knowledge-driven. However, the existing rescue decisions are mainly based on the experience of experts, which requires a large amount of professional knowledge. Furthermore, the scenes are often very complex. Different rescue methods and equipment are usually required for different scenes. Because of the above shortcomings, we propose a novel rescue decision algorithm via Earthquake Disaster Knowledge Graph Reasoning. On the one hand, the earthquake scene is modeled through the Visual Perception module to recognize the damaged state and material of the building in the image. On the other hand, an Earthquake Disaster Knowledge Graph is constructed, and the Graph mapping is used to model the Earthquake Disaster Knowledge Graph for learning a particular vector embedding of each entity. Finally, according to the prediction of the image and the representation of the entity, the Decision Reasoning module is proposed to calculate the similarity between the state and material of the building and each target decision for reasoning the most reasonable rescue measures for specific disaster scene. For the earthquake disaster rescue, we build the Disaster Image Dataset for recognizing of state and material of the building in the disaster scene and construct the Earthquake Disaster Knowledge Graph. Extensive experiments on the Disaster Image Dataset and Earthquake Disaster Knowledge Graph demonstrate the effectiveness of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig.2
Fig. 3

Similar content being viewed by others

References

  1. Hong, C., Fu, X.S., Ming, W., et al.: Study on the standard system for earthquake emergency response and rescue. China Saf. Sci. J. 22(07), 164–170 (2012)

    Google Scholar 

  2. Fan, Y.: Study on decision-making of on-site rescue plan for earthquake disaster. New Technol. New Prod. China 1, 2 (2020)

    Google Scholar 

  3. Ying, H.S., Hao, W., Dan, L., et al.: Group decision-making for earthquake emergency rescue plan based on D-S evidence theory. China Saf. Sci. J. 30(5), 6 (2020)

    Google Scholar 

  4. Ming, L., Guang, W.S.: Assessment and improvement of earthquake emergency plan. Petrochem. Saf. Technol. 25(6), 3 (2009)

    Google Scholar 

  5. Liang, G.H.: Study on the Evaluation of Rescue Ability of Earthquake Rescue Projects in China. Southwest Petroleum University, Chengdu (2015)

    Google Scholar 

  6. Yuan, P.L.: Research on earthquake emergency rescue decision based on case-based reasoning. Electron. World 5, 2 (2017)

    Google Scholar 

  7. Di, L., Qi, P.S.: Study on the decision-making model of village emergency rescue in the early stage after the earthquake. J. Eng. Manag. 5, 5 (2016)

    Google Scholar 

  8. Liu, X., Zhang, S., Wei, F., et a1.: Recognizing named entities in tweets. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 359–367. ACI, Stroudsburg, PA (2011)

  9. Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction. In Proceedings of the 42nd Association for Computational Linguistics, pp. l–22. ACI, Stroudsburg, PA (2004)

  10. Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th International ACM Conference on Research and Development in Information Retrieva1, pp. 765–774. ACM, New York (2011)

  11. Pedersen, T., Purandare, A., Kulkarni, A.: Name discrimination by clustering similar contexts. In: Proceedings of the 6th International Conference on Intelligent Text Processing and Computational Linguistics, pp. 220–231. Springer, Berlin (2005)

  12. Wang, Z., Li, J., Li, S., et al.: Cross-lingual knowledge validation based taxonomy derivation from heterogeneous online wikis. In: Proceedings of the 28th Conference on Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence. 28(1) (2014).

  13. Lu, S., Hsu, K.H., Kuo, L.: A semantic service match approach based on wordnet and SWRI rules. In: 2013 IEEE 10th International Conference on e-Business Engineering, pp. 419–422. IEEE (2013)

  14. He, W., Feng, Y., Zhao, D.: Improving knowledge base completion by incorporating implicit information. In: Joint International Semantic Technology Conference, pp. 141–153. Springer, Cham (2015)

  15. Gardner, M., Mitchell, T.: Efficient and expressive knowledge base completion using subgraph feature extraction. In: Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1488–1498. Association for Computational Linguistics, Lisbon (2015)

  16. Wang, Q., , J., Luo, Y., et al.: Knowledge base completion via coupled path ranking. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1308–1318. Association for Computational Linguistics, Berlin (2016)

  17. He, S., Liu, K., Ji, G., et al.: Learning to represent knowledge graphs with Gaussian embedding. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 623–632 (2015)

  18. Xiao, H., Huang, M., Hao, Y., et al.: From one point to a manifold: orbit models for knowledge graph embedding. arXiv:1512.04792 (2015)

  19. Zhong, W.Z., Lei, C., Xi, L.C.: A survey of knowledge reasoning based on neural network. Comput Eng Appl 55(12), 8–19, 36 (2019)

    Google Scholar 

  20. Socher, R., Chen, D.Q., Manning, C., et al.: Reasoning with neural tensor networks for knowledge base completion. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 926–934. ACM, Lake Tahoe (2013)

  21. Shi, B.X., Weninger, T.: ProjE: embedding projection for knowledge graph completion. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 1236–1242. AAAI, San Francisco (2017)

  22. Das, R., Neelakantan, A., Belanger, D., et al.: Chains of reasoning over entities, relations, and text using recurrent neural networks. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pp. 132–141. Association for Computational Linguistics, Valencia (2017)

  23. Wang, H., Li, S.Y., Pan, R., et al.: Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforcement learning. In: Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 2623–263. Association for Computational Linguistics, Hong Kong (2019)

  24. Yu, T.H.: Research on Reasoning Algorithm of Semantic Path Combination Relation in Knowledge Graph. Dalian Maritime University, Dalian (2020)

    Google Scholar 

  25. Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: Annual Conference on Artificial Intelligence (AAAI) (2011)

  26. Jenatton, R., Le, R.N., Bordes, A., Obozinski, G., et al.: A latent factor model for highly multi-relational data. In: Advances in Neural Information Processing Systems, 25 (2012)

  27. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence And Statistics. JMLR Workshop and Conference Proceedings, pp. 249–256 (2010)

  28. Hong, D.: Overview of knowledge map construction technology. J. Comput. Res. Dev. 53(3), 19 (2016)

    MathSciNet  Google Scholar 

  29. Xian, K.D.: Rescue of building collapse after earthquake. China Emerg. Rescue 6, 3 (2008)

    Google Scholar 

  30. Yi, G.L., Yuan, Z., Zhi, W.L.: Research on search and rescue strategy of collapsed buildings in earthquake site. Technol. Earthq. Disaster Prev. 5(4), 7 (2010)

    Google Scholar 

  31. Ding, F., Jian, J.R., et al.: The technology of forcible entry of buildings in the earthquake disaster. In: Academic seminar on fire fighting and emergency rescue technology (2012)

Download references

Funding

This work was supported by National Key Research and Development Program of China (No.2018AAA0102205), National Natural Science Foundation of China (61902399), Beijing Natural Science Foundation (L201001, 4222039).

Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Yifan Jiao & Sisi You

Authors
  1. Yifan Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sisi You
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yifan Jiao built the Disaster Image Dataset, constructed the Earthquake Disaster Knowledge Graph, wrote and reviewed the manuscript. Sisi You collected a part of earthquake scene images to assist to build the Disaster Image Dataset.

Corresponding author

Correspondence to Yifan Jiao.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiao, Y., You, S. Rescue decision via Earthquake Disaster Knowledge Graph reasoning. Multimedia Systems 29, 605–614 (2023). https://doi.org/10.1007/s00530-022-01002-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-022-01002-9

Keywords

Search

Navigation