@InProceedings{xiong-hoang-wang:2017:EMNLP2017,
  author    = {Xiong, Wenhan  and  Hoang, Thien  and  Wang, William Yang},
  title     = {DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {564--573},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1060}
}