An adaptive semantic model to enhance web accessibility to visually impaired users

By Sorrentino, T.; Santos, A.; Macedo, J.; Ribeiro, C.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2016

Abstract

Web has becoming an invaluable source of knowledge. However, visually impaired users have faced critical difficulties to access the services and data available on Web. To tackle this problem, this paper proposes a semantic model to improve accessibility to websites for users with visual disability. The model is made by components to identify and prioritize relevant information on web pages, converting pages elements into more understandable ones, by a strategy of semantically enrichment that results in an adapted page that meets the user's needs. The model is under development and has been partially implemented and validated in two different scenarios, one dealing with a site that portrays the Brazilian semiarid region and the other one regarding an adaptation of a social network page, replacing an image with an equivalent audio description. The experiment has shown the feasibility of semantic web technologies to improve accessibility.

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