Context-aware Temporal Knowledge Graph Completion Based on Relation Constraints

Abstract

The existing temporal knowledge graph completion models only consider the structural information of the quadruple itself,ignoring the implicit neighbor information and the constraints of relationships on entities,which leads to the poor perfor-mance of the models on the temporal knowledge graph completion task.In addition,some datasets exhibit unbalanced distribution in time,which makes it difficult for model training to achieve a good balance.To address these problems,the paper proposes a context-aware model based on relation constraints(CARC).CARC solves the problem of an unbalanced distribution of datasets in time through an adaptive time granularity aggregation module and uses a neighbor-aggregator to integrate contextual information into entity embeddings to enhance the embedding representation of the entity.In addition,the quadruple relation constraint mo-dule is designed to make the embeddings of entities with the same relational constraints close to each other,while those with diffe-rent relational constraints are far away from each other,which further enhances the embedding representation of entities.Extensive experiments are conducted on several publicly available temporal datasets,and the experimental results prove the superiority of the proposed model.

Keywords

• temporal knowledge graph|link prediction|time interval prediction|relation constraint|neighbor information|time granularity