research-article

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

Authors:

Christopher Ochs,

Mark A. MusenAuthors Info & Claims

Journal of Biomedical Informatics, Volume 62, Issue C

Pages 90 - 105

https://doi.org/10.1016/j.jbi.2016.06.008

Published: 01 August 2016 Publication History

Abstract

Display Omitted A tool for creating summaries of ontologies called abstraction networks is described.Ontologies in various formats are represented using a generic ontology framework.Abstraction networks can be derived for many ontologies using the same tool.A generic partial-area taxonomy abstraction network derivation is introduced.Live partial-area taxonomies, which update as an ontology is edited, are introduced. 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.

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Cited By

Chen JHe YGeng YJiménez-Ruiz EDong HHorrocks I(2023)Contextual semantic embeddings for ontology subsumption predictionWorld Wide Web10.1007/s11280-023-01169-926:5(2569-2591)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1007/s11280-023-01169-9
Mohsen WAref MElBahnasy K(2020)Cooperative Domain Ontology Reduction Based on Power SetsProceedings of the 6th International Conference on Frontiers of Educational Technologies10.1145/3404709.3404771(196-203)Online publication date: 5-Jun-2020
https://dl.acm.org/doi/10.1145/3404709.3404771
Zheng LYumak HChen LOchs CGeller JKapusnik-Uner JPerl Y(2017)Quality assurance of chemical ingredient classification for the National Drug File Reference TerminologyJournal of Biomedical Informatics10.1016/j.jbi.2017.07.01373:C(30-42)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.jbi.2017.07.013
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cover image Journal of Biomedical Informatics

Journal of Biomedical Informatics Volume 62, Issue C

August 2016

282 pages

ISSN:1532-0464

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Copyright © Elsevier Inc.

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Elsevier Science

San Diego, CA, United States

Publication History

Published: 01 August 2016

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Cited By

Chen JHe YGeng YJiménez-Ruiz EDong HHorrocks I(2023)Contextual semantic embeddings for ontology subsumption predictionWorld Wide Web10.1007/s11280-023-01169-926:5(2569-2591)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1007/s11280-023-01169-9
Mohsen WAref MElBahnasy K(2020)Cooperative Domain Ontology Reduction Based on Power SetsProceedings of the 6th International Conference on Frontiers of Educational Technologies10.1145/3404709.3404771(196-203)Online publication date: 5-Jun-2020
https://dl.acm.org/doi/10.1145/3404709.3404771
Zheng LYumak HChen LOchs CGeller JKapusnik-Uner JPerl Y(2017)Quality assurance of chemical ingredient classification for the National Drug File Reference TerminologyJournal of Biomedical Informatics10.1016/j.jbi.2017.07.01373:C(30-42)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.jbi.2017.07.013
Ochs CCase JPerl Y(2017)Analyzing structural changes in SNOMED CTs Bacterial infectious diseases using a visual semantic deltaJournal of Biomedical Informatics10.1016/j.jbi.2017.02.00667:C(101-116)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.jbi.2017.02.006
Elhanan GOchs CMejino JLiu HMungall CPerl Y(2017)From SNOMED CT to UberonArtificial Intelligence in Medicine10.1016/j.artmed.2017.05.00279:C(9-14)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.artmed.2017.05.002

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