Search (3 results, page 1 of 1)

  • × author_ss:"Cristo, M."
  • × year_i:[2010 TO 2020}
  1. Dalip, D.H.; Gonçalves, M.A.; Cristo, M.; Calado, P.: ¬A general multiview framework for assessing the quality of collaboratively created content on web 2.0 (2017) 0.04 
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    Date
    16.11.2017 13:04:22
    Object
    Web 2.0
  2. Souza, J.; Carvalho, A.; Cristo, M.; Moura, E.; Calado, P.; Chirita, P.-A.; Nejdl, W.: Using site-level connections to estimate link confidence (2012) 0.01 
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    Abstract
    Search engines are essential tools for web users today. They rely on a large number of features to compute the rank of search results for each given query. The estimated reputation of pages is among the effective features available for search engine designers, probably being adopted by most current commercial search engines. Page reputation is estimated by analyzing the linkage relationships between pages. This information is used by link analysis algorithms as a query-independent feature, to be taken into account when computing the rank of the results. Unfortunately, several types of links found on the web may damage the estimated page reputation and thus cause a negative effect on the quality of search results. This work studies alternatives to reduce the negative impact of such noisy links. More specifically, the authors propose and evaluate new methods that deal with noisy links, considering scenarios where the reputation of pages is computed using the PageRank algorithm. They show, through experiments with real web content, that their methods achieve significant improvements when compared to previous solutions proposed in the literature.
  3. Ferreira, R.S.; Graça Pimentel, M. de; Cristo, M.: ¬A wikification prediction model based on the combination of latent, dyadic, and monadic features (2018) 0.01 
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    Abstract
    Considering repositories of web documents that are semantically linked and created in a collaborative fashion, as in the case of Wikipedia, a key problem faced by content providers is the placement of links in the articles. These links must support user navigation and provide a deeper semantic interpretation of the content. Current wikification methods exploit machine learning techniques to capture characteristics of the concepts and its associations. In previous work, we proposed a preliminary prediction model combining traditional predictors with a latent component which captures the concept graph topology by means of matrix factorization. In this work, we provide a detailed description of our method and a deeper comparison with a state-of-the-art wikification method using a sample of Wikipedia and report a gain up to 13% in F1 score. We also provide a comprehensive analysis of the model performance showing the importance of the latent predictor component and the attributes derived from the associations between the concepts. Moreover, we include an analysis that allows us to conclude that the model is resilient to ambiguity without including a disambiguation phase. We finally report the positive impact of selecting training samples from specific content quality classes.

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