Incomplete Knowledge Graph

Incomplete knowledge graphs (IKGs) pose a significant challenge in knowledge representation and reasoning, as they lack crucial information for complete understanding and inference. Current research focuses on developing robust methods for completing IKGs and answering complex queries despite missing data, employing techniques like graph neural networks, reinforcement learning (including self-supervised approaches), and embedding methods that leverage both structural and semantic information (including type information and numerical literals). These advancements are crucial for improving the accuracy and efficiency of knowledge-based systems across various applications, including question answering, recommendation systems, and counterfactual reasoning. The ultimate goal is to build more reliable and comprehensive knowledge representations from inherently incomplete data sources.