Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies

Christopher Ochs, Zhe He, Ling Zheng, James Geller, Yehoshua Perl, George Hripcsak, Mark A. Musen

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one ontology at a time. To improve the efficiency of the Abstraction-Network-based QA methodology, we introduced a QA framework that uses uniform Abstraction Network derivation techniques and QA methodologies that are applicable to whole families of structurally similar ontologies. For the family-based framework to be successful, it is necessary to develop a method for classifying ontologies into structurally similar families. We now describe a structural meta-ontology that classifies ontologies according to certain structural features that are commonly used in the modeling of ontologies (e.g., object properties) and that are important for Abstraction Network derivation. Each class of the structural meta-ontology represents a family of ontologies with identical structural features, indicating which types of Abstraction Networks and QA methodologies are potentially applicable to all of the ontologies in the family. We derive a collection of 81 families, corresponding to classes of the structural meta-ontology, that enable a flexible, streamlined family-based QA methodology, offering multiple choices for classifying an ontology. The structure of 373 ontologies from the NCBO BioPortal is analyzed and each ontology is classified into multiple families modeled by the structural meta-ontology.

Original languageEnglish (US)
Pages (from-to)63-76
Number of pages14
JournalJournal of Biomedical Informatics
Volume61
DOIs
StatePublished - Jun 1 2016

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Keywords

  • Abstraction network
  • Family-based ontology quality assurance
  • Ontology classification
  • Structural meta-ontology

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