BERT-FKGC: Text-Enhanced Few-Shot Representation Learning for Knowledge Graphs

In recent years, few-shot knowledge graph completion (FKGC) emerged as a prominent research problem, focused on utilizing a limited number of reference entity pairs to complete triples with unseen relations. Recent studies have attempted addressing this problem by modeling interactions between head and tail entities. However, existing FKGC methods represent semantics predominantly based on the neighborhood information of entities in the knowledge graph, thus can only infer the hidden and unobserved relations within the knowledge graph, limiting their reasoning capabilities. To overcome these limitations, we introduce text descriptions to FKGC and propose BERT-FKGC, a model capable of learning the integrated distribution of both the entity text descriptions and neighborhood information. By using a gating network that allows the model to dynamically select weights, our method can flexibly combine neighborhood information and textual descriptions. Besides addressing the prediction of unseen relations, our method is also capable of representing unseen entities. To validate the effectiveness of our model, we introduce a new dataset, FB15K-237-One, which includes textual descriptions for entities. We conduct extensive experiments on the FB15K-237-One dataset to validate the superiority of BERTFKGC.