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.