Representing vaccine misinformation using ontologies

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Background: In this paper, we discuss the design and development of a formal ontology to describe misinformation about vaccines. Vaccine misinformation is one of the drivers leading to vaccine hesitancy in patients. While there are various levels of vaccine hesitancy to combat and specific interventions to address those levels, it is important to have tools that help researchers understand this problem. With an ontology, not only can we collect and analyze varied misunderstandings about vaccines, but we can also develop tools that can provide informatics solutions. Results: We developed the Vaccine Misinformation Ontology (VAXMO) that extends the Misinformation Ontology and links to the nanopublication Resource Description Framework (RDF) model for false assertions of vaccines. Preliminary assessment using semiotic evaluation metrics indicated adequate quality for our ontology. We outlined and demonstrated proposed uses of the ontology to detect and understand anti-vaccine information. Conclusion: We surmised that VAXMO and its proposed use cases can support tools and technology that can pave the way for vaccine misinformation detection and analysis. Using an ontology, we can formally structure knowledge for machines and software to better understand the vaccine misinformation domain.

Original languageEnglish (US)
Article number22
JournalJournal of Biomedical Semantics
Volume9
Issue number1
DOIs
StatePublished - Aug 31 2018
Externally publishedYes

Keywords

  • Microattribution
  • Misinformation
  • Natural language processing
  • Ontology
  • Semantic similarity
  • Semantic web
  • Vaccine

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Health Informatics
  • Computer Networks and Communications

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