Refined Information Service Using Knowledge-Base and Deep Learning to Extract Advertisement Articles from Korean Online Articles

Yongjun Kim, Yung-Cheol Byun () and Sang-Joon Lee ()
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Yongjun Kim: Computer Engineering Department, Jeju National University, Jeju 63243, Korea
Yung-Cheol Byun: Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
Sang-Joon Lee: Computer Engineering Department, Jeju National University, Jeju 63243, Korea

Sustainability, 2022, vol. 14, issue 20, 1-14

Abstract: We live amidst a flood of information in the internet and digital revolution era. Due to such indiscriminate information access, there are many problems in accurately recognizing the information desired by the user. Moreover, there are many difficulties with finding accurate information and the articles that individuals want due to indiscriminate advertisements in various online papers such as SNS and internet newspapers. Negative experiences with these advertisements lead to advertisement avoidance; if media users avoid advertisements, the media’s existence is threatened. This system aims to provide high-quality online articles, excluding promotions, by designing a system using a knowledge-based management system (KBMS) and Deep Learning system to solve the problems of advertisement. In other words, this system compares advertisement phrases or general keywords related to a specific company and product promotion with the contents to be searched in the database system of the knowledge-based management service.

Keywords: advertisement article; disguised advertisement; Deep Learning; KBMS; database (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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