Reinforcement Learning

Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multihop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop …

Reinforcement Learning based Meta-path Discovery in Large-scale Heterogeneous Information Networks

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, …