Challenges faced by authors and reviewers of encyclopedic content include the identification of which candidate concepts in one article should be linked to other articles, since concepts should be selected if they are able to provide a deeper understanding of the article topic, and ambiguity resolution. This task is called wikification. Wikification is normally addressed as a classification problem. Many supervised and semi-supervised techniques have been proposed to deal with wikification: their aim is to learn, from examples of concepts, which instances should be wikified. We observed that, although one encyclopedia can be viewed as a concept graph, supervised techniques seldom explore the richness of information provided by the graph topology: they normally only take advantage of statistical features related to concepts and the association among them. In this work, we address wikification as the problem of predicting the edges of a graph. Unlike previous approaches, our model fully explores the graph topology in the prediction task by using a matrix factorization technique. We also include in our model features related to concepts and its associations. Upon experimenting our approach with a sample of Wikipedia, we achieved gains up to 20% over a state-of-the-art baseline.

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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.

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.