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

Journal: Journal of biomedical informatics
Published Date:

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.

Authors

  • Christopher Ochs
    New Jersey Institute of Technology, Newark, NJ.
  • Zhe He
    School of Information, Florida State University, Tallahassee, FL, USA.
  • Ling Zheng
    CSSE Department, Monmouth University, West Long Branch, NJ, USA.
  • James Geller
    Dept of Computer Science, NJIT, Newark, NJ, USA.
  • Yehoshua Perl
    Dept of Computer Science, NJIT, Newark, NJ, USA.
  • George Hripcsak
    Department of Biomedical Informatics, Columbia University, 622 W 168th Street, PH20, New York, NY 10032, USA; Medical Informatics Services, NewYork-Presbyterian Hospital, 622 W 168th Street, PH20, New York, NY 10032, USA. Electronic address: hripcsak@columbia.edu.
  • Mark A Musen
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305-5479, United States. Electronic address: musen@stanford.edu.