DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
This addresses the challenge of efficient and accurate reasoning in knowledge graphs for applications like question answering and semantic search, representing a novel method for a known bottleneck.
The paper tackles the problem of learning to reason in large-scale knowledge graphs by proposing DeepPath, a reinforcement learning framework that uses a policy-based agent with continuous states based on knowledge graph embeddings to sample multi-hop relational paths, and it outperforms path-ranking and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.
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.