%0 Conference Proceedings
%T DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
%A Xiong, Wenhan
%A Hoang, Thien
%A Wang, William Yang
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F xiong-etal-2017-deeppath
%X We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector-space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.
%R 10.18653/v1/D17-1060
%U https://aclanthology.org/D17-1060
%U https://doi.org/10.18653/v1/D17-1060
%P 564-573