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        <title>An Interpretable Knowledge Transfer Model for Knowledge Base Completion</title>
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    <abstract>Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, 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.</abstract>
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%0 Conference Proceedings
%T An Interpretable Knowledge Transfer Model for Knowledge Base Completion
%A Xie, Qizhe
%A Ma, Xuezhe
%A Dai, Zihang
%A Hovy, Eduard
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F xie-etal-2017-interpretable
%X Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, 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.
%R 10.18653/v1/P17-1088
%U https://aclanthology.org/P17-1088
%U https://doi.org/10.18653/v1/P17-1088
%P 950-962

Markdown (Informal)

[An Interpretable Knowledge Transfer Model for Knowledge Base Completion](https://aclanthology.org/P17-1088) (Xie et al., ACL 2017)

• An Interpretable Knowledge Transfer Model for Knowledge Base Completion (Xie et al., ACL 2017)

ACL

• Qizhe Xie, Xuezhe Ma, Zihang Dai, and Eduard Hovy. 2017. An Interpretable Knowledge Transfer Model for Knowledge Base Completion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 950–962, Vancouver, Canada. Association for Computational Linguistics.