  Few-shot Knowledge Graph (KG) Relational Reasoning aims to predict unseen
triplets (i.e., query triplets) for rare relations in KGs, given only several
triplets of these relations as references (i.e., support triplets). This task
has gained significant traction due to the widespread use of knowledge graphs
in various natural language processing applications. Previous approaches have
utilized meta-training methods and manually constructed meta-relation sets to
tackle this task. Recent efforts have focused on edge-mask-based methods, which
exploit the structure of the contextualized graphs of target triplets (i.e., a
subgraph containing relevant triplets in the KG). However, existing
edge-mask-based methods have limitations in extracting insufficient information
from KG and are highly influenced by spurious information in KG. To overcome
these challenges, we propose SAFER (Subgraph Adaptation for Few-shot Relational
Reasoning), a novel approach that effectively adapts the information in
contextualized graphs to various subgraphs generated from support and query
triplets to perform the prediction. Specifically, SAFER enables the extraction
of more comprehensive information from support triplets while minimizing the
impact of spurious information when predicting query triplets. Experimental
results on three prevalent datasets demonstrate the superiority of our proposed
framework SAFER.
