Account of Spatio-Temporal Characteristics in Customs Anti-Smuggling Intelligence Acquisition: A Combined LSTM+CRF Model Using TF-IDF and Levenshtein

Zhanhai Yang, XuAn Wang, Mingyue Qiu, Senlin Hou and Yuqiang Wu
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Zhanhai Yang: Nanjing University, China
XuAn Wang: Engineering University of PAP, China
Mingyue Qiu: Nanjing Police University, China
Senlin Hou: Key Laboratory of Wildlife Evidence Technology State Forest and Grassland Administration, China
Yuqiang Wu: Nanjing Police University, China

International Journal of Data Warehousing and Mining (IJDWM), 2024, vol. 20, issue 1, 1-20

Abstract: The related information on smuggling crimes exists extensively in various media, with multiple data sources. Anti-smuggling intelligence faces the contradiction between the explosive growth of data size and high-efficiency intelligence judgment. Considering the current characteristics of smuggling activities, it is urgent to obtain knowledge from multi-source case data. Aiming to explore a smuggling knowledge acquisition algorithm based on deep learning, this study proposed an anti-smuggling knowledge representation model with both temporal and spatial characteristics and a knowledge-driven anti-smuggling intelligent judgment method. By combining two means, data, information, knowledge, and intelligence were effectively fused via the Term Frequency-Inverse Document Frequency (TF-IDF) technique and Levenshtein distance algorithms, promoting deep mining and application of anti-smuggling big-data resources and enhancing both automation and intelligence levels in anti-smuggling intelligence judgment.

Date: 2024
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