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Abstract

This research is focused on the continuation of the Hypertext Map prototype implementation - MHTX, proposed by Lima, (2004), with the general objective of transforming the MHTX into a semantic content management product facilitating navigation in context supported by customizable software that is easy to use, through high end desktop/web interfaces that sustain the operation of its functions. Besides, these studies aim, in the long run, to achieve the simplification of the information organization, access and recovery processes in digital libraries, making archive management by authors, content managers and information professionals possible.

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Date

22. 2.2013 11:39:23

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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.