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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (12): 142-154    DOI: 10.11925/infotech.2096-3467.2022.1140
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Research and Practice of Reasoning-Assisted Decision-Making Methods for Injury Crimes
Hua Bin,Wei Menghan()
School of Science & Technology, Tianjin University of Finance and Economics, Tianjin 300222, China
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Abstract  

[Objective] Taking the crime of intentional injury as an example, this paper proposes a reasoning and visualisation method for the cause of injury crimes based on knowledge graph and D-S evidence theory. [Methods] First, we constructed the crime knowledge ontology of intentional injury crimes and supplementing relevant knowledge. Then, we used the case trial records as a data source. Third, we used text mining technology to extract knowledge and instantiate it to form a case knowledge map. Forth, we used D-S theory to resolve evidence conflicts and complete knowledge fusion. Finally, we utilized custom inference rules to achieve and visualize the results of reasoning. [Results] The accuracy of the truth value determination by D-S evidence theory reached 95.45%, which showed the effectiveness of the proposed method. [Limitations] The method of this paper is influenced by the degree of linguistic standardisation of the interrogation records. [Conclusions] The proposed method does not limit the interrogation process and frequency, which can improve the accuracy of knowledge fusion of multiple interrogation records, form the results of case cause analysis based on objective facts, and improve the efficiency of law enforcement case handling and the level of power supervision.
Key words： Law Enforcement and Case Handling      Text Mining      Knowledge Graph      D-S Theory      Decision-Making Assistance     
Received: 31 October 2022      Published: 22 March 2023
ZTFLH:  TP391  
  D914  
Corresponding Authors: Wei Menghan，ORCID：0000-0003-4217-5880，E-mail：1254446864@qq.com。   
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Bin Hua
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Cite this article:

Hua Bin, Wei Menghan. Research and Practice of Reasoning-Assisted Decision-Making Methods for Injury Crimes. Data Analysis and Knowledge Discovery, 2023, 7(12): 142-154.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1140     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I12/142

Research Framework
类 属性
时间 具体时间，季节，旬，时段，日期类型，天气
地点 详细地址，经纬度，所在社区编码，所在街区编码，所在行政区，所在辖区，场所，特点
人 嫌疑人 姓名，别名，性别，出生日期，身份证号，体貌特征，民族，籍贯，现住址，文化程度，政治面貌，职业，健康状况，家庭成员，简历，前科
被害人 姓名，别名，性别，出生日期，身份证号，体貌特征，民族，籍贯，现住址，文化程度，政治面貌，职业，健康状况，家庭成员，简历，前科
其他涉案人员 姓名，别名，性别，出生日期，身份证号，体貌特征，民族，籍贯，现住址，文化程度，政治面貌，职业，健康状况，家庭成员，简历，前科
作案行为 名称，行为幅度，持续时长/次数，是否具有指向性，行为后是否施救，使用工具，作用部位
工具 名称，长度，宽度，重量，容量，其他特征，来源，下落，致伤度
人体部位 施害部位 名称，是否为要害
受害部位 名称，是否为要害
伤情 部位，程度
动机 主观心态 名称
犯罪目的 名称
Intentional Injury Crime Knowledge Ontological Attribute Definition Description
Conflicting Entity Interval
General Knowledge Ontology for Injury Crimes
通用关系名 语义关系描述
S U B C L A S S _ O F（包含） 父类与子类之间的
H A V E _ A C T I O N（拥有行为） 人与行为之间的关系
P E O P L E _ R E L A T I O N（人物关系） 人与人之间的关系
H A V E _ M O T I V A T I O N（拥有动机） 人与动机之间的关系
A C T I O N _ W I T H T O O L（使用工具） 行为与工具之间的关系
A C T I O N _ W I T H P O S I T I O N（使用部位） 行为与施害部位之间的关系
A C T I O N _ D O P E O P L E（作用于人） 行为与人之间的关系
A C T I O N _ D O P O S I T I O N（作用于部位） 行为与受害部位之间的关系
P R E _ A F T E R（行为先后） 行为与行为之间的关系
B E L O N G _ T O（属于） 部位与人之间的关系
H A V E _ I N J U R Y（拥有伤情） 人与伤情之间的关系
H A P P E N _ T I M E（发生时间） 时间与行为之间的关系
H A P P E N _ P L A C E（发生地点） 地点与行为之间的关系
Relationship Description of Ontology Semantic
Concept Tree of Body Part
SAO类型 对应关系
（人，行为，…） H A V E _ A C T I O N（拥有行为）
（…，行为，部位） A C T I O N _ D O P O S I T I O N（作用于部位）
（…，行为，人） A C T I O N _ D O P E O P L E（作用于人）
（人，使用触发词，工具） A C T I O N _ W I T H T O O L（使用工具）
（工具，行为，…） A C T I O N _ W I T H T O O L（使用工具）
（人，使用触发词，部位） A C T I O N _ W I T H P O S I T I O N（使用部位）
（部位，行为，部位） A C T I O N _ W I T H P O S I T I O N（使用部位）
（时间，行为，…） H A P P E N _ T I M E（发生时间）
（地点，行为，…） H A P P E N _ P L A C E（发生地点）
SAO Structure and Entity Relationship Mapping
Conflict Type Conversion
The Graph of Four Conflict Transcripts
冲突实体集示例 基于属性特征相似度的
m a s s函数		 基于实体语义相似度的
m a s s函数		 基于结构相似度的
m a s s函数		 权重 本文方法
D e m p s t e r合成
{ 胳 膊} 0.25 0.410 8 0.280 7 0.125 0.012 7
{ 头} 0.75 0.589 2 0.719 3 0.875 0.987 2
胳 膊 ， 头 0 0 0 0 0
D-S Evaluation Results
The Contents of Interrogation Transcript
Case Knowledge Graph
基于Word2Vec余弦相似度的
实体对齐方法准确率/%		 本文方法消融实验准确率/%
去除属性特征 去除实体语义 去除结构相似度 去除支持度权重 本文方法
83.46 72.72 86.36 81.83 77.27 95.45
Knowledge Convergence Results
The Visualizing of Query Results
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