Affordable Access

deepdyve-link
Publisher Website

Developing an automated mechanism to identify medical articles from wikipedia for knowledge extraction

Authors
  • Yu, Lishan1
  • Yu, Sheng2, 3, 4
  • 1 Department of Mathematical Sciences, Tsinghua University, Beijing, China
  • 2 Center for Statistical Science, Tsinghua University, Beijing, China
  • 3 Department of Industrial Engineering, Tsinghua University, Beijing, China
  • 4 Institute for Data Science, Tsinghua University, Beijing, China
Type
Published Article
Journal
International Journal of Medical Informatics
Publisher
Elsevier B.V.
Publication Date
Jul 13, 2020
Volume
141
Pages
104234–104234
Identifiers
DOI: 10.1016/j.ijmedinf.2020.104234
PMID: 32693245
PMCID: PMC7357526
Source
PubMed Central
Keywords
License
Unknown

Abstract

Wikipedia contains rich biomedical information that can support medical informatics studies and applications. Identifying the subset of medical articles of Wikipedia has many benefits, such as facilitating medical knowledge extraction, serving as a corpus for language modeling, or simply making the size of data easy to work with. However, due to the extremely low prevalence of medical articles in the entire Wikipedia, articles identified by generic text classifiers would be bloated by irrelevant pages. To control the false discovery rate while maintaining a high recall, we developed a mechanism that leverages the rich page elements and the connected nature of Wikipedia and uses a crawling classification strategy to achieve accurate classification. Structured assertional knowledge in Infoboxes and Wikidata items associated with the identified medical articles were also extracted. This automatic mechanism is aimed to run periodically to update the results and share them with the informatics community.

Report this publication

Statistics

Seen <100 times