DeepAI AI Chat
Log In Sign Up

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

07/20/2017
by   Wenhan Xiong, et al.
The Regents of the University of California
0

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 FULL TEXT

page 1

page 2

page 3

page 4

12/23/2021

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

Graph walking based on reinforcement learning (RL) has shown great succe...
06/12/2019

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Recent advances in personalized recommendation have sparked great intere...
08/31/2018

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

Multi-hop reasoning is an effective approach for query answering (QA) ov...
04/09/2020

Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion

Most of todays work on knowledge graph completion is concerned with sub-...
12/10/2017

Inducing Interpretability in Knowledge Graph Embeddings

We study the problem of inducing interpretability in KG embeddings. Spec...
07/23/2019

Efficient Knowledge Graph Accuracy Evaluation

Estimation of the accuracy of a large-scale knowledge graph (KG) often r...