Temporal Knowledge Graph Question Answering Method Based on Semantic and Structural Enhancement
Knowledge graphs,as one of the popular research topics in the field of natural language processing,have consistently received widespread attention from the academic community.In reality,the knowledge quiz process often carries temporal infor-mation.Consequently,in recent years,the application of temporal knowledge graphs for knowledge question answering has gained popularity among scholars.Traditional methods for temporal knowledge graph question answering primarily encode the question information to facilitate the inference process.However,they are unable to deal with the more complex entities and tem-poral relationships contained in the questions.To address this,semantic and structural enhancement for temporal knowledge graph question answering is proposed.This method aims to simultaneously consider both semantic and structural information in the inference process to improve the probability of providing correct answers.Firstly,implicit temporal expressions in the ques-tions are parsed,and the questions are rewritten using direct representations based on the information in the temporal knowledge graph.Additionally,the temporal information in the temporal knowledge graph is aggregated according to different time granulari-ties based on the question set.Secondly,the semantic information of the questions is represented and fused based on entity and time information to enhance the learning of entity and time semantics.Subsequently,subgraphs are extracted based on the ex-tracted entities,and the structural information of the subgraphs is captured using graph convolutional networks.Finally,the fused semantic and structural information of the questions are concatenated,and candidate answers are scored,with the entity re-ceiving the highest score selected as the answer.Comparative tests on MultiTQ data sets show that the proposed model outper-forms other baseline models.
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