title: Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.
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.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA. Electronic address: cro3@njit.edu.
authors.forename: Christopher
authors.initials: C
authors.lastname: Ochs
authors.affiliation: School of Information, Florida State University, Tallahassee, FL 32306, USA.
authors.forename: Zhe
authors.initials: Z
authors.lastname: He
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Ling
authors.initials: L
authors.lastname: Zheng
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: James
authors.initials: J
authors.lastname: Geller
authors.affiliation: Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors.forename: Yehoshua
authors.initials: Y
authors.lastname: Perl
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY 10027, USA.
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.affiliation: Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Musen
mesh_terms: D064229:Biological Ontologies; D011786:Quality Control
keywords: Abstraction network; Family-based ontology quality assurance; Ontology classification; Structural meta-ontology