OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies

• Published in: IEEE transactions on visualization and computer graphics Vol. 26; no. 1; pp. 1140 - 1150
• Main Authors: Yang, Ying, Wybrow, Michael, Li, Yuan-Fang, Czauderna, Tobias, He, Yongqun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Authoring; Biology; Complexity; Data visualization; Drugs; interactive exploration; Knowledge representation; Ontologies; Ontology; ontology associations; Ontology visualisation; Structural hierarchy; Subject specialists; Task analysis; visual compression; Visualization
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2019.2934557

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.