A Review on Ontology Learning Approaches of Creating a Topic Map of Cybercrime Research

K. J. Lee
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Abstract

Conducting an academic research requires getting a firm grasp of ongoing research issues as well as locating research materials effectively. Often research in different fields on a similar topic can assume diverse approaches due to different objectives and research goals in their own fields. Especially in an interdisciplinary research field like cybercrime, many research topics overlap with those of other research fields. Researchers in such a field, therefore, can benefit from understanding the related domains of one’s own research.  Topic maps provide methods for understanding research domain and managing relevant information resources at the same time. In this paper, we review a topic map solution to acquire knowledge structure and to locate information resources effectively. We address current problems of cybercrime research, review previous studies that use automated methods for topic map creation, and examine existing sets of methods for automatically extracting topic map components. Especially, the methods we discuss here are text mining techniques for extracting ontology components, denoted as ontology learning.
构建网络犯罪研究主题图的本体学习方法综述
进行学术研究需要对正在进行的研究问题有一个牢固的把握,并有效地定位研究材料。由于各自领域的目标和研究目标不同,在不同领域对类似主题的研究通常可以采用不同的方法。特别是在网络犯罪这样一个跨学科的研究领域,许多研究课题与其他研究领域的课题重叠。因此,这一领域的研究人员可以从了解自己研究的相关领域中受益。主题图为理解研究领域和管理相关信息资源提供了方法。本文提出了一种主题图方法来获取知识结构和有效地定位信息资源。我们讨论了网络犯罪研究的当前问题,回顾了以前使用自动化方法创建主题图的研究,并检查了自动提取主题图组件的现有方法集。特别是,我们在这里讨论的方法是用于提取本体组件的文本挖掘技术,称为本体学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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