Dissertation (May 11, 2026): Daiane de Ascenção Cardoso
Student: Daiane de Ascenção Cardoso
Title: Method for Assessing Accessibility in Biomedical Ontologies Visualizations
Advisors: Kele Teixeira Belloze (advisor) and Felipe da Rocha Henriques (co-advisor)
BCommittee: Kele Teixeira Belloze (Cefet/RJ), Felipe da Rocha Henriques (Cefet/RJ), Ingrid Monteiro (UFC), Glauco Amorim (Cefet/RJ) and Luis Carlos dos Santos Coutinho Retondaro (Cefet/RJ)
Day/Hour: May 11, 2026 / 1:30 p.m.
Room: https://teams.microsoft.com/
Abstract: Biomedical ontologies play a fundamental role in structuring and communicating scientific knowledge, yet representing them accessibly remains a challenge. This dissertation proposes a systematic and automatable method for evaluating the accessibility of ontology visualizations. A qualitative analysis through methodological triangulation (usability, communicability, and accessibility) revealed the absence of objective criteria in existing tools. Subsequently, 3,048 parameterized visualizations of the Hemoglobin class from the Sickle Cell Disease Ontology (SCDO) were generated under three contrast configurations. A sample was manually labeled using the PCHRD model (Perceivable, Comprehensible, Hierarchically clear, Reliably labeled, and Distinguishable), adapted from the Chartability framework, revealing a relevant combinatorial asymmetry: there are more ways for a visualization to be inaccessible than accessible. A Random Forest was applied in two iterations — a probe of accessibility prevalence (confirming the labeling finding) and a proof of concept with mPCHRD-driven sample curation —, indicating that visual attributes carry discriminative signal. To verify real-world applicability, a corpus of images was built from articles in the Journal of Biomedical Semantics, classified by multimodal models (Claude Haiku 4.5 and GPT-4o-mini) and manually evaluated. Finally, the Chain-of-Thought technique was adopted to inspect the models’ reasoning when applying the PCHRD criteria, comparing automatic classifications with human judgments. The results indicate that the method predicts accessibility levels from graphical features and that explicit reasoning makes the models more restrictive in identifying perceptual barriers, potentially supporting inclusive design practices in biomedical ontology visualization. This work offers a replicable protocol, formalizes PCHRD as an instrument for assessment and retrieval, and fills a gap in the quantitative measurement of accessibility in information visualization.