Knowledge Graph Completion

Knowledge graph completion (KGC) aims to infer missing relationships within knowledge graphs, improving their completeness and utility. Current research emphasizes integrating diverse knowledge sources, such as common sense reasoning, external ontologies, and large language models (LLMs), into KGC models, often employing graph neural networks, transformer architectures, and embedding methods. These advancements enhance the accuracy and efficiency of KGC, impacting various applications including question answering, recommendation systems, and risk assessment in cybersecurity. Furthermore, there's a growing focus on improving the interpretability and trustworthiness of KGC models, addressing the "black box" nature of many existing approaches.