Study on Knowledge Graph Question Answering Methods Incorporating External Knowledge
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.
knowledge graph question answeringQA contextpre-trained language modelexternal knowledge
万方数据
维普
