Analysis of cross-referencing artificial intelligence topics based on sentence modeling

Hosung Woo, Jamee Kim, Wongyu Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Artificial intelligence (AI) is bringing about enormous changes in everyday life and today's society. Interest in AI is continuously increasing as many countries are creating new AI-related degrees, short-term intensive courses, and secondary school programs. This study was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge and to provide a foundation for topic compositions to construct an academic body of knowledge of AI. To this end, machine learning-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI, such as algorithm complexity, discrete structures, fundamentals of software development, and parallel and distributed computing were identified. The results of this study provide the knowledge necessary to cultivate talent by identifying relationships with other fields in the edutech field.

Original languageEnglish
Article number3681
JournalApplied Sciences (Switzerland)
Volume10
Issue number11
DOIs
Publication statusPublished - 2020 Jun 1

Keywords

  • Cross referencing topic
  • Machine learning analysis
  • Sentence modeling
  • Topic analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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