Research on the Construction Method and Application of Case Knowledge Graph Based on Prompt Learning

doi:10.3969/j.issn.1671-1122.2024.11.016

Abstract

Abstract:

To address the inefficiencies and time-consuming nature of traditional case processing and analysis methods, this study proposed a method for constructing case knowledge graphs aimed at improving case handling efficiency and enhancing the depth and breadth of case analysis. This method provided law enforcement personnel with more comprehensive and accurate case information support. Firstly, the CasePrompt prompt learning method was integrated into the OneKE large language model, leading to the development of a case event extraction model. Secondly, based on case domain data, a conceptual layer architecture for the knowledge graph was built, and the case event extraction model was used to achieve entity extraction. Finally, the extracted structured case data was converted into triplets and stored in the Neo4j graph database, realizing the construction of a case knowledge graph based on prompt learning. Experimental results show that the prompt learning fine-tuned large model demonstrates superior event extraction performance compared to traditional deep learning models. It effectively identifies and extracts event information from case text data, enabling the construction of high-quality case knowledge graphs, thereby enhancing case analysis efficiency.

Key words: case knowledge graph, prompt learning, large language model, smart policing

CLC Number:

TP309

QIN Zhenkai, XU Mingchao, JIANG Ping. Research on the Construction Method and Application of Case Knowledge Graph Based on Prompt Learning[J]. Netinfo Security, 2024, 24(11): 1773-1782.

Figures/Tables 11

References 44

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数据集	案件类别（案件数/个）					总计
数据集	诈骗案件	勒索案件	抢劫案件	偷盗案件	贩毒案件	总计
训练集	3524	1551	545	1389	426	7435
测试集	965	428	206	429	96	2124
验证集	414	172	134	218	124	1062
总计	4903	2151	885	2036	646	10621

实验环境	环境配置
操作系统	Windows 11
CPU	Intel(R) Core(TM) i7-10750H CPU @2.60 GHz 2.59 GHz
GPU	NVIDIA A10
内存	30 GB
Python	3.10
深度学习框架	PyTorch 2.2.0
大语言模型	OneKE

参数	参数值
Epoch	10
Batch Size	32
Max Sequence Length	256
Learning Rate	3e-5

模型	P	R	F1
GraphNER	56.31%	61.43%	58.76%
TagNet-CNN	64.64%	67.93%	66.24%
Bi-LSTM-CRF	74.07%	73.24%	73.66%
Chinese-BERT-wwm	71.09%	78.40%	74.56%
OneKE	76.90%	91.07%	83.39%
OneKE+CasePrompt	85.40%	95.18%	90.03%

方法	P	R	F1
CasePrompt	85.40%	95.18%	90.03%
Instruction	76.90%	91.07%	83.39%
Prefix	79.60%	93.59%	86.03%
Examples	83.46%	93.38%	88.14%