Relational Triple Extraction

Relational triple extraction aims to automatically identify subject-relation-object triples from text, a crucial step in building knowledge graphs. Recent research focuses on improving accuracy, particularly for complex sentences and those containing multiple overlapping triples, through advanced model architectures such as joint models, diffusion models, and those incorporating contrastive learning or bidirectional tagging strategies. These advancements enhance knowledge graph construction, impacting various downstream applications including information retrieval, question answering, and natural language understanding. Furthermore, research is actively addressing challenges like handling zero-cardinality sentences and improving performance in low-resource and multilingual settings.