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    <abstract>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.</abstract>
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%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

Markdown (Informal)

[DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://aclanthology.org/D17-1060) (Xiong et al., EMNLP 2017)

• DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning (Xiong et al., EMNLP 2017)

ACL

• Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 564–573, Copenhagen, Denmark. Association for Computational Linguistics.