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Construction and Application of Knowledge Graph for Building Fire

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Abstract

The traditional storage method of fire accident cases is mainly in the form of text, and it is difficult to effectively conduct comprehensive analysis due to the limited ability to display key information and fire knowledge. In this paper, a structured storage form of building fire cases was proposed based on knowledge graph, which can comprehensively describe and visualize the fire causes, the dynamic fire development process and evacuation process. It enables readers to get information and knowledge from building fire cases intuitively, and supports the comprehensive analysis for building fire prevention strategies. The knowledge graphs are constructed for two common building types (residential and public buildings), and have the capacity to reflect the dynamic development law of fires from ignition to spread in different buildings. Meanwhile, as the occupants’ evacuation is the first concern when a fire occurs, the knowledge graphs also visualize the relationship among various conditions in the evacuation process. Different application scenarios are displayed in the paper, including case query, root-cause analysis and consequence forecasting, which shows the advantages and applicability of building fire knowledge graph.

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References

  1. Johansson N, van Hees P, Särdqvist S (2012) Combining statistics and case studies to identify and understand deficiencies in fire protection. Fire Technol 48:945–960. https://doi.org/10.1007/s10694-012-0255-z

    Article  Google Scholar 

  2. Ahmadi MT, Aghakouchak AA, Mirghaderi R et al (2020) Collapse of the 16-story plasco building in tehran due to fire. Fire Technol 56:769–799. https://doi.org/10.1007/s10694-019-00903-y

    Article  Google Scholar 

  3. Geiman JA, Lord JM (2012) Systematic analysis of witness statements for fire investigation. Fire Technol 48:219–231. https://doi.org/10.1007/s10694-010-0208-3

    Article  Google Scholar 

  4. Festag S (2021) The statistical effectiveness of fire protection measures: learning from real fires in Germany. Fire Technol 57:1589–1609. https://doi.org/10.1007/s10694-020-01073-y

    Article  Google Scholar 

  5. Scheuer S, Haase D, Meyer V (2013) Towards a flood risk assessment ontology–knowledge integration into a multi-criteria risk assessment approach. Comput Environ and Urban Syst 37:82–94

    Article  Google Scholar 

  6. Xu J, Nyerges TL, Nie G (2014) Modeling and representation for earthquake emergency response knowledge: perspective for working with geo-ontology. Int J Geogr Inf Sci 28(1):185–205

    Article  Google Scholar 

  7. Qiu L, Du Z, Zhu Q, Fan Y (2017) An integrated flood management system based on linking environmental models and disaster-related data. Environ Modell Software 91:111–126

    Article  Google Scholar 

  8. Tao K, Zhao Y, Zhu P, Zhu Y, Liu S, Zhao X (2020) Knowledge graph construction for integrated disaster reduction. Geomatics Inf Sci Wuhan Univ 45(8):1296–1302

    Google Scholar 

  9. Wang H, Han Z, Yunqing B (2019) Construction of causality event evolutionary graph of aviation accident. In: 2019 5th International conference on transportation information and safety (ICTIS), pp 692–697

  10. Li S, Zhang Y, Liu J, Cui X, Zhang Y (2021) Recommendation model based on public neighbor sorting and sampling of knowledge graph. J Electron Inf Technol 43(12):3522–3529

    Google Scholar 

  11. Xiang W (2020) Overview of construction technology and application of event knowledge graph. Comput Mod 01:10–16

    Google Scholar 

  12. Liu J, Schmid F, Li K, Zheng W (2021) A knowledge graph-based approach for exploring railway operational accidents. Reliab Eng Syst Saf 207:107352

    Article  Google Scholar 

  13. Zhu G, Zhang M, Yi Y (2022) Prediction of evolution results of urban rail transit emergencies based on knowledge graph. J Electron Inf Technol 44:1–9

    Google Scholar 

  14. Li J, Chen W (2018) Visualization analysis of research status of building fire. Fire Sci Technol 37(02):250–254

    Google Scholar 

  15. Li J, Liu J, Wang J, Feng C (2019) Academic map of fire safety science based on the fire safety journal. Fire Sci Technol 38(12):1760–1765

    Google Scholar 

  16. Xiang Y, Chen Y, Qian Y, Qin Y (2022) Knowledge mapping analysis of fire risk assessment research based on CiteSpace. Fire Sci Technol 41(4):486–490

    Google Scholar 

  17. Chen F, Yan H, Ma X, Wang Y, Zhao R, Chen X, Jia L (2022) Construction and application of knowledge graph for urban rail fire accident. In: Proceedings of the 5th international conference on electrical engineering and information technologies for rail transportation (EITRT) 2021, Singapore, pp 558–572

  18. Yan H, Ma X, Chen F, Zhao R, Jia L (2022) Knowledge modeling and analysis for railway fire accident using ontology-based knowledge graph. In: Proceedings of the 5th international conference on electrical engineering and information technologies for rail transportation (EITRT) 2021, Singapore, pp 573–591

  19. Singhal A (2012) Introducing the knowledge graph: things, not strings. In: Official blog of google [online]. http://googleblog.blogspot.be/2012/05/introducing-knowledge-graph-things-not.html, Accessed 01 May 2021

  20. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43

    Article  Google Scholar 

  21. Xu Z, Sheng Y, He L, Wang Y (2016) Review on knowledge graph techniques. J Univ Electron Sci Technol China 45(4):589–606

    Google Scholar 

  22. Lu F, Yu L, Qiu P (2017) On geographic knowledge graph. Int J Geogr Inf Sci 19(6):723–734

    Google Scholar 

  23. Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) Dbpedia: A nucleus for a web of open data. In: The semantic web: 6th international semantic web conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, pp 722–735

  24. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp 1247–1250

  25. Vrandečić D, Krötzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78–85

    Article  Google Scholar 

  26. Hoffart J, Suchanek FM, Berberich K, Weikum G (2013) YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif Intell 194:28–61

    Article  MathSciNet  Google Scholar 

  27. Zhao Z, Han SK, So IM (2018) Architecture of knowledge graph construction techniques. Int J Pure Appl Math 118(19):1869–1883

    Google Scholar 

  28. Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing? Int J Hum-Comput Stud 43(5–6):907–928

    Article  Google Scholar 

  29. Studer R, Benjamins VR, Fensel D (1998) Knowledge engineering: principles and methods. Data Knowl Eng 25(1–2):161–197

    Article  Google Scholar 

  30. Faming G, Ruran L (2018) Research on ontology data storage of massive petroleum field based on Neo4j. Comput Sci 45(s1):549–554

    Google Scholar 

  31. Liu Q, Li Y, Duan H, Liu Y, Qin Z (2016) Knowledge graph construction techniques. J Comput Res Dev 53(3):582–600

    Google Scholar 

  32. Rospocher M, Vossen P, Fokkens A, Aldabe I, Rigau G, Bogaard T (2016) Building event-centric knowledge graphs from news. J Web Semant 37:132–151

    Article  Google Scholar 

  33. Chen P, Lu Y, Zheng VW, Chen X, Yang B (2018) Knowedu: a system to construct knowledge graph for education. IEEE Access 6:31553–31563

    Article  Google Scholar 

  34. Shekarpour S, Saxena A, Thirunarayan K, Shalin VL, Sheth A (2018) Principles for developing a knowledge graph of interlinked events from news headlines on twitter. arXiv preprint arXiv:1808.02022

  35. Zhao S, Wang Q, Massung S, Qin B, Liu T, Wang B, Zhai C (2017) Constructing and embedding abstract event causality networks from text snippets. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 335–344

  36. Wang C, Ma X, Chen J, Chen J (2018) Information extraction and knowledge graph construction from geoscience literature. Comput Geosci 112:112–120

    Article  Google Scholar 

  37. Zhang L, Glänzel W, Liang L (2009) Tracing the role of individual journals in a cross-citation network based on different indicators. Scientometrics 81(3):821–838

    Article  Google Scholar 

  38. Shu X, Yan J, Hu J, Wu J, Deng B (2020) Risk assessment model for building fires based on a Bayesian network. J Tsinghua Univ Sci Technol 60(4):321–327

    Google Scholar 

  39. Cheng H, Hadjisophocleous GV (2011) Dynamic modeling of fire spread in building. Fire Saf J 46(4):211–224

    Article  Google Scholar 

  40. Hu J, Xie X, Shu X, Shen S, Ni X, Zhang L (2022) Fire risk assessments of informal settlements based on fire risk index and Bayesian network. Int J Env Res Pub He 19(23):15689

    Article  Google Scholar 

  41. FSB (Fire Service Bureau) (2012–2018) China fire yearbook. Yunnan Personnel Press, Kunming

  42. Ministry of Housing and Urban-Rural Development of the People’s Republic of China (2014) Code for fire protection design of building (GB 50016-2014)

  43. Devlin J, Chang M W, Lee K, et al. (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  44. Brown T, Mann B, Ryder N et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877–1901

    Google Scholar 

  45. Sun Y, Wang S, Feng S, et al. (2021) Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. arXiv preprint arXiv:2107.02137

  46. Baidu (2023) ERNIE Bot. https://yiyan.baidu.com/ Accessed 2 Nov 2023

  47. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  48. Hu J, Shu X, Shen S et al (2022) A method to improve the determination of ignition probability in buildings based on Bayesian network. Fire Mater 46(4):666–676

    Article  Google Scholar 

  49. Liao L (2015) Application research of Neo4j in spatio-temporal data storage. Cybers Secur 6(08):43–44

    Google Scholar 

  50. Francis N, Green A, Guagliardo P, Libkin L, Lindaaker T, Marsault V, Taylor A (2018) Cypher: an evolving query language for property graphs. In: Proceedings of the 2018 international conference on management of data, pp 1433–1445

  51. Emergency Management Bureau of Anyang (2016) Investigation report on “6.25” fire accident in Zhengzhou City [online]. https://yjj.anyang.gov.cn/2016/08-20/2229002.html, Accessed 14 May 2022

  52. Emergency Management Bureau of Heilongjiang (2018) Investigation report on “8.25” fire accident in Beilong Tangquan Hotel, Harbin [online]. http://yjgl.hlj.gov.cn/yjgl/c104120/201810/c00_30101763.shtml, Accessed 15 May 2022

  53. Manes M, Rush D (2019) A critical evaluation of BS PD 7974-7 structural fire response data based on USA fire statistics. Fire Technol 55(4):1243–1293. https://doi.org/10.1007/s10694-018-0775-2

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by National Natural Science Foundation of China (Grant No: 72204139), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515110049), and Young Innovative Talents Project from Department of Education of Guangdong Province (Grant No. 2022KQNCX157).

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Authors and Affiliations

  1. Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai, 519087, China

    Jun Hu, Xiaoyong Ni & Yongsheng Yang

  2. School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, 519087, China

    Jun Hu, Xiaoyong Ni & Yongsheng Yang

  3. School of Safety Science, Tsinghua University, Beijing, 100084, China

    Xueming Shu, Xuecai Xie & Shifei Shen

  4. Beijing Key Laboratory of City Integrated Emergency Response Science, Tsinghua University, Beijing, 100084, China

    Xueming Shu, Xuecai Xie & Shifei Shen

  5. School of National Safety and Emergency Management, Beijing Normal University at Zhuhai, Yanhua Building, Room 104, Zhuhai, China

    Jun Hu

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  1. Jun Hu
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Correspondence to Jun Hu.

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Appendix

Appendix

In this appendix, the entities and relations in the building fire knowledge graph and their attributes are explained, as shown in Tables A1 and A2.

Table A1 Description of Entities in the Building Fire Knowledge Graph
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Table A2 Description of Relations in the Building Fire Knowledge Graph
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Hu, J., Shu, X., Xie, X. et al. Construction and Application of Knowledge Graph for Building Fire. Fire Technol 60, 1711–1739 (2024). https://doi.org/10.1007/s10694-024-01544-6

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