Relational Knowledge

Relational knowledge, focusing on how entities and their relationships are represented and reasoned about in artificial intelligence systems, is a crucial area of research aiming to improve knowledge representation and reasoning capabilities. Current efforts concentrate on evaluating and improving how large language models (LLMs) and knowledge graphs (KGs) encode and utilize relational information, often employing techniques like knowledge probing, contrastive learning, and graph neural networks to analyze and enhance these representations. This research is significant because improved relational knowledge handling is vital for advancing various applications, including question answering, knowledge base completion, and enhancing the perception capabilities of autonomous systems.