COVID-19 Deep Clustering: An Ontology construction clustering method with dynamic medical labeling

Cong Phuoc Phan, Jung Hsien Chiang

研究成果: Conference contribution

摘要

This paper introduces a novel clustering-based framework for COVID-19 ontology construction using Pubmed LitCovid scientific research articles data. Our study uses a semantic approach with hierarchical clustering to construct a more effective COVID-19 documents ontology with medical labeling and search. We believe this study may initiate a future development for an advanced COVID-19 domain-specific ontology. The significant contribution from this research addresses solving the limitations in manual classification tasks of the everyday fast-increasing number of scientific papers and the overloading of their unclassified knowledge. With this research, our provision will help scholars with a better search mechanism to retrieve highly relevant expert information about their favorite topics in the COVID-19-related literature. To our best knowledge, this approach is the first successful attempt to apply auto clustering with labeling and search on the COVID-19 research papers. Moreover, in text processing, we propose a systematical evaluation without dependence on standard data collection to evaluate our methodology.

原文English
主出版物標題SoICT 2022 - 11th International Symposium on Information and Communication Technology
發行者Association for Computing Machinery
頁面216-222
頁數7
ISBN(電子)9781450397254
DOIs
出版狀態Published - 2022 12月 1
事件11th International Symposium on Information and Communication Technology, SoICT 2022 - Hanoi, Viet Nam
持續時間: 2022 12月 12022 12月 3

出版系列

名字ACM International Conference Proceeding Series

Conference

Conference11th International Symposium on Information and Communication Technology, SoICT 2022
國家/地區Viet Nam
城市Hanoi
期間22-12-0122-12-03

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面
  • 電腦視覺和模式識別
  • 電腦網路與通信

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