KG Completion

Knowledge graph completion (KGC) aims to infer missing relationships between entities within a knowledge graph, improving its completeness and utility. Current research emphasizes addressing challenges like data sparsity, particularly for newly introduced entities ("inductive KGC"), and mitigating biases inherent in embedding models trained on potentially skewed data. This involves developing novel architectures, such as those incorporating graph neural networks, normalizing flows, and leveraging large language models for knowledge extraction and reasoning, often focusing on improving accuracy and explainability. Improved KGC techniques have significant implications for various applications, including enhanced information retrieval, more robust AI systems, and improved analysis of complex datasets.