Few-shot, especially one-shot learning is a prominent
research area in the field of knowledge graphs (KGs), aiming
to utilize a limited number of triples with unseen relations as
reference information for inferring missing knowledge. Recent
research focuses on improving the semantic representation of
entity pairs using interactions between their head and tail entities.
However, this method only considers the reference information
as the measurement criterion without taking into account the
potential impact of it on the reasoning process of the model. In
this paper, we propose a novel method that utilizes factual information
interactions. Firstly, we learn static representations of
entities based on their neighborhood information. Subsequently,
we learn relation adaptive representations by incorporating the
reference information. This interactive modeling strengthens the
association between entity representations and task relations
while suppressing irrelevant relations. Extensive experiments
demonstrate that our model outperforms state-of-the-art methods
on two public datasets. Remarkably, on the NELL-One dataset
for one-shot link prediction, our model achieves an improvement
of 11.8% in MRR compared to the best baseline model.