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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (7): 73-84    DOI: 10.11925/infotech.2096-3467.2018.1269
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Constructing Domain Ontology for Intelligent Applications: Case Study of Anti Tele-Fraud
Shiqi Deng,Liang Hong()
School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper tries to build domain ontology for intelligent applications, aiming to enhance the capability of domain knowledge representing and application development. [Methods] We proposed the application-driven circulation method to model cross-domain knowledge based on the demands of intelligent applications. It has the structure of “requirement + construction + evaluation”, so that requirements play leading role in ontology construction. We took the field of anti telephone fraud as an example, and constructed the anti-fraud ontology of the intelligent requirements. [Results] Our anti-fraud domain ontology represented a wide range of cross-domain knowledge and effectively supported intelligent anti-fraud applications, which were based on the semantics of fraudulent calls. [Limitations] More research is needed to examine the requirements of intelligent applications. [Conclusions] The proposed method promotes more research in domain ontology construction and anti-fraud methods.
Key words： Ontology Construction      Intelligent Application      Ontology Evaluation      Anti Telephone Fraud     
Received: 14 November 2018      Published: 06 September 2019
:  G350  
Corresponding Authors: Liang Hong     E-mail: hong@whu.edu.cn
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Shiqi Deng
Liang Hong

Cite this article:

Shiqi Deng,Liang Hong. Constructing Domain Ontology for Intelligent Applications: Case Study of Anti Tele-Fraud. Data Analysis and Knowledge Discovery, 2019, 3(7): 73-84.

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http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1269     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I7/73

专业概念 概念定义
诈骗事件 电话诈骗事件的简称, 包含行为人、通话行为与内容、诈骗流程、时空特征等, 反诈本体对事件各部分进行 建模。
诈骗场景 诈骗分子用于诈骗的背景信息, 如冒充公检法类型的电话诈骗场景为公检法, 是区分诈骗事件类型的主要依据。
诈骗流程 诈骗分子实施诈骗的过程, 由具有时序性的各步骤组成, 是诈骗手段的集中体现、诈骗事件分析的核心。
反向数据 电话诈骗事件及其通话数据, 诈骗分子在通话时提供的信息, 如电话、身份、操作等用于诈骗的虚假信息。
正向数据 诈骗场景相关的各领域的正确规范信息, 如公检法机构的正常电话、地点、权限等数据。
评估指标 计算公式 含义
关系
丰富度		 $RR=(\left| P \right|)/(\left| SC \right|+\left| P \right|)$ (1) 反映关系的多样性, 以及各类关系在本体中的分布情况; 拥有除继承关系外更多其他关系的本体, 往往比仅有继承关系的能表达出更丰富的信息。
其中, $\left| SC \right|$继承关系数量; $\left| P \right|$为除继承关系外的其他关系数量
属性
丰富度		 $AR=(\left| att \right|)/(\left| C \right|)$ (2) 类的属性多少关系到类的相关信息能否被充分表示, 一般而言, 属性越多的本体内涵越丰富, 本体质量越高。
其中, $\left| att \right|$为所有类的属性总量; $\left| C \right|$为类的总量
继承关系
丰富度		 $I{{R}_{C}}=({{\mathop{\sum }^{}}_{{{C}_{i}}\in C}}|{{H}^{C}}({{C}_{1}},{{C}_{i}})|)/(\left| C \right|)$ (3) 描述本体中不同层次继承关系的数量, 刻画本体的体系结构形态: 继承层次多但各类子类少的本体为垂直型, 反之为水平型。
其中, $|{{H}^{C}}({{C}_{1}},{{C}_{i}})|)$为每个类${{C}_{i}}$的子类${{C}_{1}}$的数量; $\left| C \right|$为类的总量
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