arxivst stuff from arxiv that you should probably bookmark

An Interpretable Knowledge Transfer Model for Knowledge Base Completion

Abstract · Apr 19, 2017 19:35 ·

projection regularities relations entity nguyen relation itransf transe cs-cl cs-ai cs-lg

Arxiv Abstract

  • Qizhe Xie
  • Xuezhe Ma
  • Zihang Dai
  • Eduard Hovy

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 perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets—WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.

Read the paper (pdf) »