Abstract
In recent years, massive multi-source heterogeneous South China Sea data have been widely used in the construction of South China Sea digital resources, such as the South China Sea Sovereign Evidence Chain Project. Due to the data sparsity, a large number of isolated data are generated, which seriously affects the analysis effect of the South China Sea Big Data. In this paper, we proposed a novel data association method. We collected data from the South China Sea Library Digital Resources as South China Sea evidence data, which is a sentence or paragraph containing time, place, people, institutions and events can prove the sovereignty of the South China Sea. According to the definition of the evidence weight by the International Court of Justice, the logical relationship of South China Sea evidence data was constructed. Firstly, we randomly selected 3068 data from 21174 evidence data to label the logical relationship. Secondly, we used the BERT pre-training model to extract the logical relationship of evidence data. Finally, the Knowledge Graph technology is used to retrieve and visualize the logical relationship of evidence data. In this paper, we applied the BERT to extract the logical relationships of evidence data with an accuracy of 0.78, which indicates that the model has some feasibility. This paper could help to improve the correlation of the South China Sea Big Data and to enhance the ability of data processing.
The authors extend their appreciation to the Young Foundation of Ministry of Education Project of Humanities and Social Sciences in China (Grand Nos: 22YJC870012), the General Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province in China (Grand Nos: 2022SJYB0444) and the School Research Foundation Project of Nanjing Institute of technology in China(Grand Nos: YKJ202231).
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Yufang, P., Hao, X., Weijian, J., Haiping, Y. (2023). Logical Relationship Extraction of Multimodal South China Sea Big Data Using BERT and Knowledge Graph. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_16
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