Lations for Knowledge Base Completion

In this work we present a novel approach for the utilization of observed relations between entity pairs in the task of triple argument prediction. The approach is based on representing observations in a shared, continuous vector space of structured relations and text. Results on a recent benchmark dataset demonstrate that the new model is superior to existing sparse feature models. In combination with state-of-the-art models, we achieve substantial improvements when observed relations are available.