An Interpretable Knowledge Transfer Model for Knowledge Base Completion

Qizhe Xie, Xuezhe Ma, Zihang Dai +1

Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to pe…