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Modifier Ontologies for frequency, certainty, degree, and coverage phenotype modifier.

Lorena Endara1, Anne E Thessen2, Heather A Cole3

  • 1University of Florida, Gainesville, United States of America University of Florida Gainesville United States of America.

Biodiversity Data Journal
|December 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces two modifier ontologies for taxonomic descriptions, aiding computable data conversion and consistent term usage in scientific literature.

Keywords:
Modifier Ontologycertainty modifierscoverage modifiersdegree modifiersfrequency modifiersliterary warrantphenotype modifiersuser consensususer warrant

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Area of Science:

  • Bioinformatics
  • Ontology Engineering
  • Taxonomy

Background:

  • Phenotypic character descriptions in literature often include modifiers (e.g., frequency, certainty, degree, coverage) that require standardization for computable data.
  • Existing ontologies primarily address spatial and method modifiers, necessitating development for other modifier types.
  • Standardizing modifiers is crucial for converting narrative descriptions into computable data and guiding future scientific publications.

Purpose of the Study:

  • To develop two novel modifier ontologies for taxonomic descriptions, focusing on frequency, certainty, degree, and coverage.
  • To establish sequential relationships between modifier terms to enable classification of annotated data.
  • To provide a consensus-based approach for modifier term usage in scientific literature.

Main Methods:

  • Two ontology designs were proposed: a closed ordered list (five-bin) and an open ordered list.
  • CharaParser was utilized to extract 130 candidate modifier terms from taxonomic descriptions.
  • Four expert curators reviewed and categorized terms using the Ontology Term Organizer (OTO), addressing inter-curator variations.

Main Results:

  • Two ontologies (closed and open list designs) were developed using the Protege ontology editor and made available as OWL files.
  • Expert categorization revealed high agreement, with 16 out of 20 bins showing full consensus, supporting a five-bin differentiation.
  • Annotation properties were used to manage semantic ambiguity and guide user caution with certain terms.

Conclusions:

  • The developed ontologies represent the first consensus-based approach for common taxonomic modifiers.
  • The five-bin ontology has been integrated into the Explorer of Taxon Concepts toolkit for character similarity computation.
  • These ontologies will enhance consistency in modifier term usage through an ontology-informed authoring tool for taxonomists.