Relational Triple

Relational triples, representing structured relationships between entities (subject, predicate, object), are fundamental units for knowledge graph construction and various natural language processing tasks. Current research focuses on improving the accuracy and efficiency of relational triple extraction from text and images, employing diverse approaches such as region-based table filling, graph neural networks, and contrastive learning within various model architectures including large language models and diffusion models. These advancements enhance knowledge base population, improve information retrieval, and enable more sophisticated applications in areas like scene graph generation and video understanding.