The continuous evolution of research has led to an exponential growth of the scientific literature. This engenders difficulties for researchers to entirely capture the most salient efforts related to their own research. In this paper, we propose a novel knowledge model for unveiling meaningful and labeled relations among articles based on both topics and latent citation dependencies. An experimentation on the whole literature in the Computer Science field allowed us to validate our approach by bridging the gap between few lines of textual content (e.g., an abstract) to the most relevant papers to be included in the bibliography.
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