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J Biomed Inform. 2016 Aug;62:90-105. doi: 10.1016/j.jbi.2016.06.008. Epub 2016 Jun 23.

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.

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Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA. Electronic address:
Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA.


Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving "live partial-area taxonomies" is demonstrated.


Abstraction network derivation; Ontology exploration; Ontology summarization; Ontology tools; Visualization of ontology content

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