融合语义结构感知与动态序列建模的蛋鸡疫病知识联合抽取模型OA

Aiming to address the challenges of overlapping entities and long-range dependencies in constructing a knowledge graph for diagnosing laying hens'diseases,syntactic and graph for hen disease joint extraction(SynGraph-HenJE),a joint extraction model that integrated semantic-structural awareness with dynamic sequence modeling was proposed.The model employed the pre-trained bidirectional encoder representations from transformers(BERT)architecture to perform semantic encoding on text data related to poultry diseases.To enhance this semantic representation,a custom semantic-guided edge-aware GATv2(SGEA-GATv2)module was introduced,which strengthened the model's ability to distinguish overlapping entities and improve relational discrimination.Through a relationship-aware gating mechanism,it integrated local modeling extracted by a temporal convolutional network(TCN)with global modeling extracted by a bidirectional long short-term memory(BiLSTM)network,effectively enhancing the model's capacity to capture long-range dependencies in laying hens'disease texts.The proposed model was evaluated on both the self-constructed laying hens'disease dataset and two public datasets.The results showed that the model achieved accuracies of 95.0%,84.2%,and 82.5%,recall rates of 92.5%,81.5%,and 78.5%,and F1-scores of 93.7%,82.8%,and 80.4%,respectively.Compared with the reference model,the proposed model demonstrated an average accuracy improvement of 7.71 percentage points,an average recall improvement of 4.58 percentage points,and an average F1 score improvement of 6.16 percentage points,demonstrating the strong generalization capability of the proposed model.SynGraph-HenJE achieved accuracies above 90%across all six entity categories.For the five relation categories,the model also outperformed the baselines,with all accuracy values exceeding 88.0%.The research effectively addressed the challenges of entity overlap and long-range dependencies,improving the quality of triplet extraction from laying hens'disease texts and providing technical support for intelligent diagnosis.