On making web accessibility more accessible: strategy and tools for social good

The world currently faces a paradox in terms of accessibility for people with disabilities. While digital technologies hold immense potential to improve their quality of life, the majority of web content still exhibits critical accessibility issues. This PhD thesis addresses this challenge by proposing two interconnected research branches. The first introduces a groundbreaking approach to improving web accessibility by rethinking how it is approached, making it more accessible itself. It involves the development of: 1. AX, a declarative framework of web components that enforces the generation of accessible markup by means of static analysis. 2. An innovative accessibility testing and evaluation methodology, which communicates test results by exploiting concepts that developers are already familiar with (visual rendering and mouse operability) to convey the accessibility of a page. This methodology is implemented through the SAHARIAN browser extension. 3. A11A, a categorized and structured collection of curated accessibility resources aimed at facilitating their intended audiences discover and use them. The second branch focuses on unleashing the full potential of digital technologies to improve accessibility in the physical world. The thesis proposes the SCAMP methodology to make scientific artifacts accessible to blind, visually impaired individuals, and the general public. It enhances the natural characteristics of objects, making them more accessible through interactive, multimodal, and multisensory experiences. Additionally, the prototype of \gls{a11yvt}, a system supporting accessible virtual tours, is presented. It provides blind and visually impaired individuals with features necessary to explore unfamiliar indoor environments, while maintaining universal design principles that makes it suitable for usage by the general public. The thesis extensively discusses the theoretical foundations, design, development, and unique characteristics of these innovative tools. Usability tests with the intended target audiences demonstrate the effectiveness of the proposed artifacts, suggesting their potential to significantly improve the current state of accessibility.

Supporting requirement elicitation and ontology testing in knowledge graph engineering

Knowledge graphs and ontologies are closely related concepts in the field of knowledge representation. In recent years, knowledge graphs have gained increasing popularity and are serving as essential components in many knowledge engineering projects that view them as crucial to their success. The conceptual foundation of the knowledge graph is provided by ontologies. Ontology modeling is an iterative engineering process that consists of steps such as the elicitation and formalization of requirements, the development, testing, refactoring, and release of the ontology. The testing of the ontology is a crucial and occasionally overlooked step of the process due to the lack of integrated tools to support it. As a result of this gap in the state-of-the-art, the testing of the ontology is completed manually, which requires a considerable amount of time and effort from the ontology engineers. The lack of tool support is noticed in the requirement elicitation process as well. In this aspect, the rise in the adoption and accessibility of knowledge graphs allows for the development and use of automated tools to assist with the elicitation of requirements from such a complementary source of data. Therefore, this doctoral research is focused on developing methods and tools that support the requirement elicitation and testing steps of an ontology engineering process. To support the testing of the ontology, we have developed XDTesting, a web application that is integrated with the GitHub platform that serves as an ontology testing manager. Concurrently, to support the elicitation and documentation of competency questions, we have defined and implemented RevOnt, a method to extract competency questions from knowledge graphs. Both methods are evaluated through their implementation and the results are promising.