To interact with webpages, people with low vision typically rely on screen magnifier assistive technology that enlarges screen content and also enables them to pan the content to view the different portions of a webpage. Our experiments prove that our proposal can beat the state-of-the-art proposal in terms of both effectiveness and efficiency the key difference is that our proposal is totally unsupervised, whereas the state-of-the-art proposal is supervised. It is novel in that it relies on an entropy-preservation metaphor that has proven to work very well on two large collections of real-world tables from the Wikipedia and the Dresden Web Table Corpus. The problem is addressed using a clustering approach that is known to be NP using standard computers, but our proposal can solve it in polynomial time, which implies a significant performance improvement. In this article, we present a new unsupervised proposal that uses a hybrid approach in which a standard computer is used to perform pre- and post-processing tasks and a quantum computer is used to perform the core task: guessing whether the cells have labels or values.
This has motivated many authors to work on proposals to extract them as automatically as possible. This facilitates rendering them, but obfuscates their structure and makes it difficult for automated business processes to leverage them. The Web provides many data that are encoded using HTML tables. Mediante la revisión bibliográfica multidisciplinar, como método principal, las conclusiones apuntan a una importante presencia de una visualidad dependiente de las máquinas inteligentes, que aportan un mayor enriquecimiento del estudio tanto de la naturaleza humana como de la realidad social en el entorno virtual. En este contexto, la investigación busca revelar la emergencia de una nueva interpretación de la visualidad, concretamente, mediante el análisis de dos líneas principales (cuya relación se trata de mostrar): por una parte, la visión artificial y su extensión en el universo posinternet de las redes sociales y de la web, donde la imagen pierde su significado simbólico y su dimensión estética para valorarse como una información que cambia el estado de un sistema y, por otro lado, el conocimiento social del mundo virtual a través del uso, la actitud y el comportamiento humano con los algoritmos inteligentes. Our experiments on a large set of Web databases show that the proposed vision-based approach is highly effective for deep Web data extraction.Įn el marco de los estudios visuales, se observa un desarrollo de singulares prácticas cuya orientación tecnológica está basada en la innovación de algoritmos de inteligencia artificial. We also propose a new evaluation measure revision to capture the amount of human effort needed to produce perfect extraction.
This approach primarily utilizes the visual features on the deep Web pages to implement deep Web data extraction, including data record extraction and data item extraction. In this paper, a novel vision-based approach that is Web-page-programming-language-independent is proposed. This motivates us to seek a different way for deep Web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep Web pages. As the popular two-dimensional media, the contents on Web pages are always displayed regularly for users to browse. Until now, a large number of techniques have been proposed to address this problem, but all of them have inherent limitations because they are Web-page-programming-language-dependent. Extracting structured data from deep Web pages is a challenging problem due to the underlying intricate structures of such pages.
Deep Web contents are accessed by queries submitted to Web databases and the returned data records are enwrapped in dynamically generated Web pages (they will be called deep Web pages in this paper).
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