Triple Extraction

Triple extraction, the task of identifying subject-predicate-object relationships from text, is crucial for building knowledge graphs and automating knowledge acquisition. Current research focuses on improving the accuracy and efficiency of extraction, particularly from complex sentences, using large language models (LLMs) augmented with retrieval mechanisms or collaborating with smaller, specialized models. These advancements leverage techniques like bidirectional tagging, contrastive learning, and graph neural networks to overcome limitations of previous methods, leading to improved performance on benchmark datasets and enabling more robust applications in diverse fields like biomedicine.