一句话：提出AXERAY，通过层次化视觉分析推断网页区域的语义分组与语义角色，实现语义级Web无障碍检测。
网页无障碍要求应用能被残障用户使用，正逐渐从可忽略的附加项变成必须满足的法律要求。然而现有Web无障碍测试工具大多只做基础语法检查，无法评估残障用户真正依赖的高层语义属性，因此无障碍评估在很大程度上仍依赖人工且费时费力。本文提出AXERAY，一种通过层次化视觉分析自动推断网页各区域语义分组及其语义角色的方法，并在此基础上检测语义层面的无障碍失败。作者在30个真实网站上评估该方法，结果表明语义分组推断的平均F-measure达到87%，无障碍失败检测准确率为85%，说明视觉层次分析可有效支撑自动化语义无障碍测试。
Web accessibility, the design of web apps to be usable by users with disabilities, impacts millions of people around the globe. Although accessibility has traditionally been a marginal afterthought that is often ignored in many software products, it is increasingly becoming a legal requirement that must be satisfied. While some web accessibility testing tools exist, most only perform rudimentary syntactical checks that do not assess the more important high-level semantic aspects that users with disabilities rely on. Accordingly, assessing web accessibility has largely remained a laborious manual process requiring human input. In this paper, we propose an approach, called AXERAY, that infers semantic groupings of various regions of a web page and their semantic roles. We evaluate our approach on 30 real-world websites and assess the accuracy of semantic inference as well as the ability to detect accessibility failures. The results show that AXERAY achieves, on average, an F-measure of 87% for inferring semantic groupings, and is able to detect accessibility failures with 85% accuracy.