[HTML][HTML] A systematic literature review of reinforcement learning-based knowledge graph research
Abstract Knowledge graphs (KGs) model entities or concepts and their relations in a
structural manner. The incompleteness has turned out to be the main challenge that hinders …
structural manner. The incompleteness has turned out to be the main challenge that hinders …
A review of knowledge graph-based reasoning technology in the operation of power systems
R Liu, R Fu, K Xu, X Shi, X Ren - Applied Sciences, 2023 - mdpi.com
Knowledge graph (KG) technology is a newly emerged knowledge representation method in
the field of artificial intelligence. Knowledge graphs can form logical map**s from cluttered …
the field of artificial intelligence. Knowledge graphs can form logical map**s from cluttered …
Incorporating logic rules with textual representations for interpretable knowledge graph reasoning
Abstract Reasoning on knowledge graphs (KGs) is significant for downstream applications,
such as question answering and information extraction. On the basis of using factual triples …
such as question answering and information extraction. On the basis of using factual triples …
Comprehensible artificial intelligence on knowledge graphs: A survey
Artificial Intelligence applications gradually move outside the safe walls of research labs and
invade our daily lives. This is also true for Machine Learning methods on Knowledge …
invade our daily lives. This is also true for Machine Learning methods on Knowledge …
An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization
M Gao, X Feng, H Yu, X Li - Applied Intelligence, 2023 - Springer
Large-scale sparse multiobjective optimization problems (SMOPs) widely exist in academic
research and engineering applications. The curse of dimensionality and the fact that most …
research and engineering applications. The curse of dimensionality and the fact that most …
Multi-fidelity information fusion with hierarchical surrogate guided by feature map**
Multi-fidelity information fusion has attracted increasing attention in the recent for its
promising in engineering design and optimization. However, most of the existing fusion …
promising in engineering design and optimization. However, most of the existing fusion …
Enriching recommendation models with logic conditions
This paper proposes RecLogic, a framework for improving the accuracy of machine learning
(ML) models for recommendation. It aims to enhance existing ML models with logic …
(ML) models for recommendation. It aims to enhance existing ML models with logic …
Making it tractable to catch duplicates and conflicts in graphs
This paper proposes an approach for entity resolution (ER) and conflict resolution (CR) in
large-scale graphs. It is based on a class of Graph Cleaning Rules (GCRs), which support …
large-scale graphs. It is based on a class of Graph Cleaning Rules (GCRs), which support …
Making It Tractable to Detect and Correct Errors in Graphs
This article develops Hercules, a system for entity resolution (ER), conflict resolution (CR),
timeliness deduction (TD), and missing value/link imputation (MI) in graphs. It proposes …
timeliness deduction (TD), and missing value/link imputation (MI) in graphs. It proposes …
Mining Path Association Rules in Large Property Graphs (with Appendix)
How can we mine frequent path regularities from a graph with edge labels and vertex
attributes? The task of association rule mining successfully discovers regular patterns in item …
attributes? The task of association rule mining successfully discovers regular patterns in item …