An expert study on hierarchy comparison methods applied to biological taxonomies curation
- Published
- Accepted
- Subject Areas
- Bioinformatics, Human-Computer Interaction, Visual Analytics
- Keywords
- Hierarchy comparison, Biological taxonomy, Information visualization, User study, Qualitative research
- Copyright
- © 2019 Sancho-Chavarria 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
- 2019. An expert study on hierarchy comparison methods applied to biological taxonomies curation. PeerJ Preprints 7:e27903v1 https://doi.org/10.7287/peerj.preprints.27903v1
Abstract
Comparison of hierarchies aims at identifying differences and similarities between two or more hierarchical structures. In the biological taxonomy domain, comparison is indispensable for the reconciliation of alternative versions of a taxonomic classification. Biological taxonomies are knowledge structures that may include large amounts of nodes (taxa), which are typically maintained manually. We present the results of a user study with taxonomy experts that evaluates four well-known methods for the comparison of two hierarchies, namely, edge drawing, matrix representation, animation, and agglomeration. Each of these methods is evaluated with respect to seven typical biological taxonomy curation tasks. To this end, we designed an interactive software environment through which expert taxonomists performed exercises representative of the considered tasks. We evaluated participants’ effectiveness and level of satisfaction from both quantitative and qualitative perspectives. Overall quantitative results evidence that participants were less effective with agglomeration whereas they were more satisfied with edge drawing. Qualitative findings reveal a greater preference among participants for the edge drawing method. Also, from the qualitative analysis, we obtained insights that contribute to explain the differences between the methods and provide directions for future research.
Author Comment
This is a submission to PeerJ Computer Science for review.