A Scalable Probabilistic Description Logic Approach for Semantic Link Prediction

Predicting potential links between unconnected nodes in a network, as collaboration networks, is a problem of great practical interest. Link prediction is mostly based on graph-based features and recently, on approaches that consider semantics of the domain. However, there is uncertainty in these predictions and considering it, can improve the prediction results. In this paper, we propose an algorithm for link prediction that uses a probabilistic ontology described with the probabilistic description logic CRALC. Moreover, our approach is scalable through a combination with graph-based features. A dataset based on the Lattes curriculum platform is used to evaluate empirically our proposal.