Abstract
Web Accessibility is a hot topic today. Striving for social inclusion has resulted in the requirement of providing accessible content to all users. However, since each user is unique, and the Web evolves in a decentralized way, little or none is known about the shape of the Web’s accessibility on its own at a large scale, as well as from the point-of-view of each user. In this chapter the authors present the Web Accessibility Knowledge Framework as the foundation for specifying the relevant information about the accessibility of a Web page. This framework leverages Semantic Web technologies, side by side with audience modeling and accessibility metrics, as a way to study the Web as an entity with unique accessibility properties dependent from each user’s point of view. Through this framework, the authors envision a set of queries that can help harnessing and inferring this kind of knowledge from Web graphs.
TopBackground
Two main research topics have influence and contribute to the study of Web accessibility on large scale: the analysis of accessibility compliance of a Web page (or Web site), and the analysis of the Web’s graph structure.
The Web Accessibility Initiative (WAI, n.d.) of the World Wide Web Consortium (W3C, n.d.) has strived for setting up the pace of Web Accessibility guidelines and standards, as a way to increase accessibility awareness to Web developers, designers, and usability experts.
The main forces of WAI are the Web Content Accessibility Guidelines, WCAG (Chisholm et al., 1999). WCAG defines a set of checkpoints to verify Web pages for specific issues that have impact on accessibility of contents, such as finding if images have equivalent textual captions. These guidelines have been updated to their second version (Caldwell et al., 2008) to better handle the automation of accessibility assessment procedures, thus dismissing the requirement of manual verification of checkpoint compliance.
Until recently, the results of accessibility assessment were presented in a human-readable format (i.e., Web page). While this is useful for developers and designers in general, this is of limited use for comparison and exchange of assessment results. Therefore, WAI has defined EARL, Evaluation and Report Language (Abou-Zahra, 2007), a standardized way to express evaluation results, including Web accessibility evaluations, in an OWL-based format (Dean & Schreiber, 2004).
EARL affords the full description of Web accessibility assessment scenarios, including the specification of who (or what) is performing the evaluation, the resource that is being evaluated, the result, and the criteria used in the evaluation.
Key Terms in this Chapter
Metric: A quantification procedure based on several criteria. In the context of this Chapter, metrics quantify accessibility based on different accessibility checkpoints.
Accessibility: The ability to access. Often tied to people with disabilities (e.g., total blindness), accessibility thrives to break the barriers to information access. We follow the strict sense of accessibility by embracing any situation where the ability to access information can be disrupted by device or even surrounding environment constraints.
Universal Usability: A research field that studies the adequacy of user interfaces and information to all users, regardless of their characteristics, knowledge, or mean of interaction (Shneiderman, 2000).
Checkpoint: A concrete verification task that materializes a (part of a) guideline. Checkpoints can be fully automated if application technology provides corresponding support (e.g., verifying if all images have associated textual captions).
Accessibility Guidelines: A set of best practices that must be followed by designers and developers when implementing software solutions (e.g., Web site) that will help on providing accessible information. By being guidelines, it should not be assumed that content is accessible just by following them.