Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Extracting Specific Information in Web Pages Using Machine Learning

머신러닝을 이용한 웹페이지 내의 특정 정보 추출

  • Lee, Joung-Yun (Industrial and Management Engineering, Incheon National University) ;
  • Kim, Jae-Gon (Industrial and Management Engineering, Incheon National University)
  • 이정윤 (인천대학교 산업경영공학과) ;
  • 김재곤 (인천대학교 산업경영공학과)
  • Received : 2018.11.26
  • Accepted : 2018.12.13
  • Published : 2018.12.31
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Abstract

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.

Keywords

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