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Accessibility should be a part of the Web design process instead of being a post-design repair process. Thus, it should be more integrated within the internal authoring tools' mechanism of generating new accessible Web contents. Web pages are usually composed of small pieces of HTML code which, dynamically nested and combined, generate full Web pages. This Web composition, specially when creating Web pages from data extracted from heterogeneous or external sources, should have accessibility into account in order to guarantee that the final page being constructed is accessible. This paper presents the set of rules that, in a Web composition process, a design tool must follow in order to create accessible Web pages. These rules are formalized with W3C standards like XPath and XQuery expressions. We also present WSLS as an accessibility enabled authoring tool that makes this task feasible, and focus in how this tool incorporates accessibility into the process of generating new Web contents.

The main approach of traditional information retrieval (IR) is to examine how many words from a query appear in a document. A drawback of this approach, however, is that it may fail to detect relevant documents where no or only few words from a query are found. The semantic analysis methods such as LSA (latent semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to address the issue, but their performance is not superior compared to common IR approaches. Here we present a query-document similarity measure motivated by the Word Mover's Distance. Unlike other similarity measures, the proposed method relies on neural word embeddings to compute the distance between words. This process helps identify related words when no direct matches are found between a query and a document. Our method is efficient and straightforward to implement. The experimental results on TREC Genomics data show that our approach outperforms the BM25 ranking function by an average of 12% in mean average precision. Furthermore, for a real-world dataset collected from the PubMed R search logs, we combine the semantic measure with BM25 using a learning to rank method, which leads to improved ranking scores by up to 25%. This experiment demonstrates that the proposed approach and BM25 nicely complement each other and together produce superior performance .

Web accessibility consists on a set of checkpoints which are rather expensive to evaluate or to spot. However, using W3C technologies, this cost can be clearly minimized. This article presents a W3C formalized rule-set version for automatable checkpoints from WCAG 1.0.

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