Contextual Associations Between Webpage Elements for Web Accessibility: An Empirical Study

2026-06-25

AI Summary

This study addresses the challenge faced by screen reader users when encountering isolated accessible names such as “Read more,” which often lack sufficient contextual information. To tackle this issue, the authors propose constructing a web page graph based on the accessibility tree, integrating semantic and spatial features derived from both the DOM and CSS to model human-perceived element associations via link prediction. The work introduces the first dataset of web page contextual relationships annotated by human evaluators and systematically evaluates the performance of MLP, GCN, GAT, and SEAL models under leave-one-site-out cross-validation using Hit@K and MRR metrics. Preliminary experiments across five websites demonstrate that the proposed models achieve Hit@10 scores ranging from 0.16 to 0.85, substantially outperforming heuristic baselines (0.08–0.30), thereby validating the approach’s effectiveness and its potential for cross-site generalization.

Abstract

[Context] Screen reader users navigating webpages by element list often encounter accessible names such as "Read more" that are valid under the W3C Accessible Name and Description Computation specification but uninterpretable in isolation. The surrounding elements that would make these names meaningful exist in the page but are not linked to the target by any mechanism. No prior work has empirically studied how to select which surrounding elements are contextually relevant to a given target. [Objective] This registered report investigates whether human-perceived contextual associations between webpage elements can be recovered from the accessibility tree using link prediction, and whether the learned associations generalize across websites. [Method] We will construct a dataset of human-annotated contextual associations on 35 websites, stratified across the Tranco top-million list, with three independent annotators per page. Each page is represented as a graph derived from its accessibility tree, augmented with spatial and semantic features from the DOM and CSS. We compare four machine learning models (MLP, GCN, GAT, and SEAL) against two heuristic baselines under leave-one-site-out cross-validation with a pre-registered statistical framework, using Hit@K and MRR. [Results] We have conducted a five-site author-annotated pilot study to establish the pipelines and parameterize the power simulation, with pilot Hit@10 ranging from 0.16 to 0.85 across four learned models and 0.08 to 0.30 across two heuristic baselines. The final results will be reported after the planned experiments and analyses are completed. [Conclusion] The study contributes a human-annotated dataset of contextual associations on webpages, an empirical evaluation of link prediction for context selection on accessibility-tree graphs, and a cross-site generalization analysis.

Problem

• web accessibility
• contextual associations
• screen reader
• accessible names
• element context

Innovation

• contextual association
• accessibility tree
• link prediction
• web accessibility
• graph neural networks