Abstract: Existing knowledge graph completion models learn a single static feature representation for entities and relationships by integrating multi-source information.But they can't represent the subtle meaning and dynamic attributes of entities and relationships that appear in different contexts.That is,entities and relationships will show different attributes,because they have different roles and meanings when they are involved in different triples.To solve above problems,an adaptive attention network for knowledge graph completion is proposed,which uses adaptive attention to model the contribution of each task-specified feature dimension,and generates dynamic and variable embedding representations for target entities and relationships.Specifically,the proposed model defines the neighbor encoder and the path aggregator to process two structures in the entity neighborhood subgraph,adaptively learn the attention weights to capture the most logically related features of the task,and to give the entities and relationships with fine-grained semantics in line with the current task.Experimental results in link prediction task show that,the MeanRank of the proposed model on FB15K-237 dataset is 6.9% lower than PathCon,and Hits@1 is 2.3% higher than PathCon.For the sparse datasets NELL-995 and DDB14,its Hits@1 reaches 87.9% and 98% respectively.Therefore,it proves that the introduction of adaptive attention mechanism can effectively extract the dynamic attributes of entities and relationships to generate a more comprehensive embedding representation,and improves the accuracy of knowledge graph completion.

Key words: Adaptive attention, Knowledge graph completion, Knowledge representation, Neighborhood subgraph