Knowledge Base Completion

Knowledge base completion (KBC) aims to fill gaps in structured knowledge repositories by predicting missing relationships between entities. Current research heavily utilizes large language models (LLMs) and other machine learning techniques, including embedding models, graph neural networks, and classification-based approaches like XGBoost, often incorporating pre-training and multimodal data fusion to improve accuracy, particularly for long-tail entities and zero-shot scenarios. These advancements are crucial for enhancing the completeness and utility of knowledge graphs, impacting various applications such as improved search engines, risk assessment in cybersecurity, and more effective question answering systems. A key challenge remains addressing biases inherent in training data and developing robust methods for handling incomplete or inconsistent information.