
A Review on Ontology Learning Approaches of Creating a Topic Map of Cybercrime Research
Author(s) -
Kijung Lee
Publication year - 2019
Publication title -
international journal of computer and technology
Language(s) - English
Resource type - Journals
ISSN - 2277-3061
DOI - 10.24297/ijct.v18i0.8063
Subject(s) - ontology , field (mathematics) , computer science , data science , grasp , cybercrime , domain (mathematical analysis) , world wide web , information retrieval , the internet , philosophy , epistemology , mathematical analysis , mathematics , pure mathematics , programming language
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.