Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies

Ling Zheng, Yan Chen, Gai Elhanan, Yehoshua Perl, James Geller, Christopher Ochs

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In previous research, we have demonstrated for a number of ontologies that structurally complex concepts (for different definitions of “complex”) in an ontology are more likely to exhibit errors than other concepts. Thus, such complex concepts often become fertile ground for quality assurance (QA) in ontologies. They should be audited first. One example of complex concepts is given by “overlapping concepts” (to be defined below.) Historically, a different auditing methodology had to be developed for every single ontology. For better scalability and efficiency, it is desirable to identify family-wide QA methodologies. Each such methodology would be applicable to a whole family of similar ontologies. In past research, we had divided the 685 ontologies of BioPortal into families of structurally similar ontologies. We showed for four ontologies of the same large family in BioPortal that “overlapping concepts” are indeed statistically significantly more likely to exhibit errors. In order to make an authoritative statement concerning the success of “overlapping concepts” as a methodology for a whole family of similar ontologies (or of large subhierarchies of ontologies), it is necessary to show that “overlapping concepts” have a higher likelihood of errors for six out of six ontologies of the family. In this paper, we are demonstrating for two more ontologies that “overlapping concepts” can successfully predict groups of concepts with a higher error rate than concepts from a control group. The fifth ontology is the Neoplasm subhierarchy of the National Cancer Institute thesaurus (NCIt). The sixth ontology is the Infectious Disease subhierarchy of SNOMED CT. We demonstrate quality assurance results for both of them. Furthermore, in this paper we observe two novel, important, and useful phenomena during quality assurance of “overlapping concepts.” First, an erroneous “overlapping concept” can help with discovering other erroneous “non-overlapping concepts” in its vicinity. Secondly, correcting erroneous “overlapping concepts” may turn them into “non-overlapping concepts.” We demonstrate that this may reduce the complexity of parts of the ontology, which in turn makes the ontology more comprehensible, simplifying maintenance and use of the ontology.

Original languageEnglish (US)
Pages (from-to)135-149
Number of pages15
JournalJournal of Biomedical Informatics
Volume83
DOIs
StatePublished - Jul 2018

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Keywords

  • Abstraction network
  • Family-based ontology quality assurance
  • National Cancer Institute thesaurus
  • Ontology auditing
  • Ontology quality assurance
  • SNOMED CT

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