On today's Web, designers take huge efforts to create visually rich websites that boast a magnitude of interactive elements. Contrarily, most web information extraction (WIE) algorithms are still based on attributed tree methods which struggle to deal with this complexity. In this paper, we introduce a versatile model to represent web documents. The model is based on gestalt theory principles-trying to capture the most important aspects in a formally exact way. It (i) represents and unifies access to visual layout, content and functional aspects; (ii) is implemented with semantic web techniques that can be leveraged for i.e. automatic reasoning. Considering the visual appearance of a web page, we view it as a collection of gestalt figures-based on gestalt primitives-each representing a specific design pattern, be it navigation menus or news articles. Based on this model, we introduce our WIE methodology, a re-engineering process involving design patterns, statistical distributions and text content properties. The complete framework consists of the UOM model, which formalizes the mentioned components, and the MANM layer that hints on structure and serialization, providing document re-packaging foundations. Finally, we discuss how we have applied and evaluated our model in the area of web accessibility.

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In this paper, we address automatic identification of common functional structures on web pages, a fundamental problem for web automation applications and graphical user interface testing. In contrast to other approaches, we aim to identify relevant patterns without relying on the source code of a web page or keywords, utilizing mostly geometrical and visually perceptible properties. We achieve this by transforming pages into an independent geometrical representation, on top of which we extract a set of features that allows us to employ traditional machine learning techniques for the identification task. We evaluate this approach by analyzing three typical scenarios, reviewing the obtained information retrieval key metrics and estimating the relevance of the chosen features. Our initial results demonstrate the feasibility of the proposed approach.