Knowledge graph question answering is one of the hot research areas in the field of natural language processing.Existing methods face two main challenges:difficulty in understanding complex natural language questions and limited semantic interpretation of entity repre-sentations.To address these challenges,a knowledge graph question answering method called DEK-KGQA is proposed,which integrates ex-ternal knowledge.First,a joint graph is constructed by combining the question knowledge graph subgraph and the QA context.Then,the rele-vance scores of nodes in the joint graph are calculated using pre-trained language models.Finally,external knowledge is introduced to en-hance information interaction and reasoning ability during the question answering process.Experimental validation is conducted on the Com-monsenseQA dataset,comparing the proposed method with existing methods.The results demonstrate that the proposed method achieves better performance in commonsense question answering tasks,validating its effectiveness.In addition,ablation experiments are conducted to evalu-ate the impact of each component on the overall performance.
关键词
知识图谱问答/QA上下文/预训练语言模型/外部知识
Key words
knowledge graph question answering/QA context/pre-trained language model/external knowledge
维普
万方数据