With the increasing need for inclusive and user-friendly technology, web
accessibility is crucial to ensuring equal access to online content for
individuals with disabilities, including visual, auditory, cognitive, or motor
impairments. Despite the existence of accessibility guidelines and standards
such as Web Content Accessibility Guidelines (WCAG) and the Web Accessibility
Initiative (W3C), over 90\% of websites still fail to meet the necessary
accessibility requirements. For web users with disabilities, there exists a
need for a tool to automatically fix web page accessibility errors. While
research has demonstrated methods to find and target accessibility errors, no
research has focused on effectively correcting such violations. This paper
presents a novel approach to correcting accessibility violations on the web by
modifying the document object model (DOM) in real time with foundation models.
Leveraging accessibility error information, large language models (LLMs), and
prompt engineering techniques, we achieved greater than a 51\% reduction in
accessibility violation errors after corrections on our novel benchmark:
ACCESS. Our work demonstrates a valuable approach toward the direction of
inclusive web content, and provides directions for future research to explore
advanced methods to automate web accessibility.

通过实时修改文档对象模型和基础模型，利用较大型语言模型和提示工程技术，该论文提出了一种纠正网络可访问性违规的新方法，在我们的新基准 ACCESS 上，经过纠正后可将可访问性违规错误减少超过 51％，展示了创造包容性网络内容的有价值方法，并为未来研究提供了探索自动化网络可访问性的高级方法的方向。

自动化网络无障碍违规修复的 Prompt 工程

ACCESS: Prompt Engineering for Automated Web Accessibility Violation  Corrections

The rapid growth of web pages and the increasing complexity of their
structure poses a challenge for web mining models. Web mining models are
required to understand the semi-structured web pages, particularly when little
is known about the subject or template of a new page. Current methods migrate
language models to the web mining by embedding the XML source code into the
transformer or encoding the rendered layout with graph neural networks.
However, these approaches do not take into account the relationships between
text nodes within and across pages. In this paper, we propose a new approach,
ReXMiner, for zero-shot relation extraction in web mining. ReXMiner encodes the
shortest relative paths in the Document Object Model (DOM) tree which is a more
accurate and efficient signal for key-value pair extraction within a web page.
It also incorporates the popularity of each text node by counting the
occurrence of the same text node across different web pages. We use the
contrastive learning to address the issue of sparsity in relation extraction.
Extensive experiments on public benchmarks show that our method, ReXMiner,
outperforms the state-of-the-art baselines in the task of zero-shot relation
extraction in web mining.

本文提出了一种基于 DOM 树短路路径编码，并结合多个页面上文本节点出现的频率以及对比学习处理关系稀疏问题的新方法 ReXMiner，用于零样本关系抽取，并在公共基准测试上表现出胜过现有方法的效果。

基于相对 XML 路径的多模态方法实现 Web 挖掘中零样本关系抽取

Towards Zero-shot Relation Extraction in Web Mining: A Multimodal  Approach with Relative XML Path

Identifying high-quality webpages is fundamental for real-world search
engines, which can fulfil users' information need with the less cognitive
burden. Early studies of \emph{webpage quality assessment} usually design
hand-crafted features that may only work on particular categories of webpages
(e.g., shopping websites, medical websites). They can hardly be applied to
real-world search engines that serve trillions of webpages with various types
and purposes. In this paper, we propose a novel layout-aware webpage quality
assessment model currently deployed in our search engine. Intuitively, layout
is a universal and critical dimension for the quality assessment of different
categories of webpages. Based on this, we directly employ the meta-data that
describes a webpage, i.e., Document Object Model (DOM) tree, as the input of
our model. The DOM tree data unifies the representation of webpages with
different categories and purposes and indicates the layout of webpages. To
assess webpage quality from complex DOM tree data, we propose a graph neural
network (GNN) based method that extracts rich layout-aware information that
implies webpage quality in an end-to-end manner. Moreover, we improve the GNN
method with an attentive readout function, external web categories and a
category-aware sampling method. We conduct rigorous offline and online
experiments to show that our proposed solution is effective in real search
engines, improving the overall usability and user experience.

本文提出了一种基于布局的网络页面质量评估模型，采用 Document Object Model（DOM）树形数据作为输入，并使用图神经网络（GNN）方法优化质量评估，并经过离线和在线实验证明该方法有效。