From SNOMED CT to Uberon: Transferability of evaluation methodology between similarly structured ontologies

Gai Elhanan, Christopher Ochs, Jose L.V. Mejino, Hao Liu, Christopher J. Mungall, Yehoshua Perl

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Objective To examine whether disjoint partial-area taxonomy, a semantically-based evaluation methodology that has been successfully tested in SNOMED CT, will perform with similar effectiveness on Uberon, an anatomical ontology that belongs to a structurally similar family of ontologies as SNOMED CT. Method A disjoint partial-area taxonomy was generated for Uberon. One hundred randomly selected test concepts that overlap between partial-areas were matched to a same size control sample of non-overlapping concepts. The samples were blindly inspected for non-critical issues and presumptive errors first by a general domain expert whose results were then confirmed or rejected by a highly experienced anatomical ontology domain expert. Reported issues were subsequently reviewed by Uberon's curators. Results Overlapping concepts in Uberon's disjoint partial-area taxonomy exhibited a significantly higher rate of all issues. Clear-cut presumptive errors trended similarly but did not reach statistical significance. A sub-analysis of overlapping concepts with three or more relationship types indicated a much higher rate of issues. Conclusions Overlapping concepts from Uberon's disjoint abstraction network are quite likely (up to 28.9%) to exhibit issues. The results suggest that the methodology can transfer well between same family ontologies. Although Uberon exhibited relatively few overlapping concepts, the methodology can be combined with other semantic indicators to expand the process to other concepts within the ontology that will generate high yields of discovered issues.

Original languageEnglish (US)
Pages (from-to)9-14
Number of pages6
JournalArtificial Intelligence in Medicine
Volume79
DOIs
StatePublished - Jun 2017

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Artificial Intelligence

Keywords

  • Anatomy ontology
  • Disjoint abstraction network
  • Overlapping concepts
  • Quality assurance
  • Semantic complexity

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