Abstract: The knowledge graph needs to face complicated real world information in construction process,and cannot model all knowledge,so it needs to be completed.Many relations in real knowledge graph often have only few entity pairs for training.Therefore,few-shot knowledge graph completion is a very significant problem.At present,embedding-based methods generally aggregate entity context information through attention mechanism or other methods,and complete knowledge graph by learning relation embeddings.These methods only consider the matching degree at relation level.Although they can predict unknown relations,the result is often not accurate.Therefore,an adaptive context matching network(ACMN) is proposed for few-shot know-ledge graph completion.Firstly,a common-neighbor awareness-encoder is proposed to aggregate the references context,that is,one-hop neighbor entities,and obtain common-neighbor awareness embeddings.Secondly,a task-related entity encoder is proposed to mine the similarity information between task entity context and common context,distinguish the contribution of one-hop neighbors to the current task,and enhance task entity representation.Then a context-relation encoder is proposed to obtain dynamic relation representations.Finally,the matching degree of entity context and relations is comprehensively considered through weighted summation to complete the completion.ACMN comprehensively evaluates whether the query triples are tenable from two aspects of entity context similarity and relations matching,which can effectively improve the prediction accuracy in few-shot scena-rios.Compared with other eight widely used algorithms on the two public data sets,ACMN achieves the best completion results in the case of different few-shot sizes.

Key words: Graph competion, Few-shot learning, Entity context, Relation prediction, Representation learning