Embedding Entity Pairs through Observed Relations for Knowledge Base Completion
Authors: Dirk Weissenborn

Abstract: 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.