Papers › DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Prediction | NELL-995 | RL | Mean AP | 79.6 | #4 of 4 | Archive leaderboard | report |
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