Construction and application of earthquake disaster knowledge graph fusing with multimodal data

WU Qirui; TIAN Miao; XIE Zhong; QIU Qinjun; CHEN Zhanlong; TAO Liufeng

doi:10.19509/j.cnki.dzkq.tb20240334

Volume 44 Issue 4

Aug. 2025

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Article Navigation > Bulletin of Geological Science and Technology > 2025 > 44(4): 90-106

WU Qirui，TIAN Miao，XIE Zhong，et al. Construction and application of earthquake disaster knowledge graph fusing with multimodal data[J]. Bulletin of Geological Science and Technology，2025，44（4）：90-106 doi: 10.19509/j.cnki.dzkq.tb20240334

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Construction and application of earthquake disaster knowledge graph fusing with multimodal data

doi: 10.19509/j.cnki.dzkq.tb20240334

WU Qirui^a
,,
TIAN Miao^b,
XIE Zhong^{a, b, c, d},
QIU Qinjun^{b, c, d
,
,},
CHEN Zhanlong^{b, c, d},
TAO Liufeng^{b, c, d}

a .
School of Future Technology, China University of Geosciences, Wuhan 430074, China
b .
Key Laboratory of Geological Survey and Evaluation, Ministry of Education
c .
School of Computer and Science
d .
Key Laboratory of Resource Quantitative Evaluation and Information Engineering, Ministry of Natural Resources, China University of Geosciences (Wuhan), Wuhan 430074, China

More Information

Author Bio:
E-mail：qrwu@cug.edu.cn
Corresponding author: E-mail：qiuqinjun@cug.edu.cn
Received Date: 17 Jun 2024
Accepted Date: 02 Jan 2025
Rev Recd Date: 10 Aug 2024

Available Online: 07 Mar 2025

Abstract

Abstract

Objective
Earthquake disaster observation data are multi-source and heterogeneous, with scattered knowledge and poor correlation, making it difficult to efficiently utilize the data for information integration and efficient querying, thereby limiting its effectiveness in supporting risk assessment and emergency decision-making.
Methods
Knowledge graphs are an effective means of data association and fusion. Firstly, based on a top-down approach, the concepts in the earthquake disaster domain were sorted out, and the ontologies of earthquake disaster data, geological/geographical environment, earthquake disaster events, earthquake disaster emergency tasks, and earthquake disaster models were constructed to form the earthquake disaster ontology layer. Combined with a bottom-up approach, a high-quality data layer was built. Through convolutional neural networks, changes in remote sensing images before and after disasters were identified, achieving intelligent structured conversion from image information to textual knowledge. The fine-tuned UIE (universal information extraction) pre-training model was used to extract named entities and relationship attribute knowledge from text data, with precision rates of 82.04% and 70.66%, respectively. Data fusion and unified expression were achieved by calculating the semantic similarity of word vectors.
Results
Taking the earthquake in Jishishan County, Linxia Prefecture, Gansu Province on December 18, 2023 as an example, a high-quality earthquake disaster knowledge graph was established through ontology construction, data extraction, and unified expression, achieving the transformation from multi-source heterogeneous earthquake data to unified knowledge representation.
Conclusion
Based on the constructed earthquake disaster knowledge graph, queries and displays of disaster losses and emergency chain decision support are realized. Additionally, potential secondary disasters can be inferred and queried by integrating relevant geological data. This method combines deep learning and pre-training techniques with multi-modal data to construct an earthquake disaster knowledge graph, providing auxiliary support for rapid and accurate earthquake disaster information queries and the prediction of secondary disasters.
- Jieshishan earthquake,
- earthquake disaster knowledge graph,
- information querying,
- pre-trained model,
- multimodel date

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References

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