Supervised approach to rank predicted links using interestingness measures

For the last decade, the automatic generation of hypothesis from the literature has been widely studied. One common approach is to model biomedical literature as a concept network; then a prediction model is applied to predict the future relationships (links) between pairs of concept. Typically, this link prediction task can be cast into in one of two forms: (a) predict the future links for a specific concept (node) or (b) predict the future links for the entire network. However, while being able to accurately forecast future relationships is vital, another, equally important question should be addressed: of the predicted links, which will be most important and/or most relevant? Attempts to answer these questions in the past have generally been domain specific. In this paper, we propose a domain-independent, supervised method that predicts the rank of future links utilizing objective interestingness measures. The results, based on analysis of thirteen common interestingness measures, indicate that, while predicting the specific future interestingness values is difficult, our approach allowed us to capture the relative ordering of the links with low error.