DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

EMNLP 2017  ·  Wenhan Xiong, Thien Hoang, William 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... 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. read more

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Datasets


Introduced in the Paper:

NELL-995

Results from the Paper


 Ranked #1 on Link Prediction on NELL-995 (Mean AP metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Link Prediction NELL-995 RL Mean AP 79.6 # 1

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