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Knowledge bases are important resources for a variety of natural languageprocessing tasks but suffer from incompleteness. We propose a novel embeddingmodel, \emph{ITransF}, to perform knowledge base completion. Equipped with asparse attention mechanism, ITransF discovers hidden concepts of relations andtransfer statistical strength through the sharing of concepts. Moreover, thelearned associations between relations and concepts, which are represented bysparse attention vectors, can be interpreted easily. We evaluate ITransF on twobenchmark datasets---WN18 and FB15k for knowledge base completion and obtainsimprovements on both the mean rank and Hits@10 metrics, over all baselines thatdo not use additional information.

An Interpretable Knowledge Transfer Model for Knowledge Base Completion