DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

EMNLP 2017 Wenhan XiongThien HoangWilliam Yang Wang

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... (read more)

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