Exploring biomedical ontology mappings with graph theory methods
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Science
- Keywords
- ontology evolution, biomedical ontology, ontology mappings, semantic web, graph theory
- Copyright
- © 2017 Kocbek et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Exploring biomedical ontology mappings with graph theory methods. PeerJ Preprints 5:e2715v1 https://doi.org/10.7287/peerj.preprints.2715v1
Abstract
Background
In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies.
Methods
We report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature.
Results
With comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Ontology mapping information
Mappings between pairs of ontologies for different versions of graphs.