Abstract:  A model referred to as subgraph structure semantic enhancement for few-shot knowledge graph completion is proposed in addressing the limitations of insufficient semantic representation of entities in few-shot learning contexts. First, an attention mechanism is employed to extract text semantic features of relation interaction, and to extract subgraph structure semantic features of clustering coefficients. Subsequently, entity semantic aggregation is executed through the utilization of a feedforward neural network and a Transformer network is applied to encode triples. Finally, the score for link prediction is computed using the prototype matching network. Experimental results show the proposed model’s superiority over metric-learning-based baseline models, outperforming the latest meta-learning-based baseline model in Hits@ 1 index on the NELL-One dataset. Moreover, across all indices on the Wiki-One dataset, the proposed model delivers optimal results. This demonstrates the proposed model’s effectiveness in enhancing entity representation and improving prediction accuracy.

Key words: few-shot learning contexts ,  knowledge graph completion , clustering coefficient, structural semantics, attention mechanism