  The ability to reason over learned knowledge is an innate ability for humans
and humans can easily master new reasoning rules with only a few
demonstrations. While most existing studies on knowledge graph (KG) reasoning
assume enough training examples, we study the challenging and practical problem
of few-shot knowledge graph reasoning under the paradigm of meta-learning. We
propose a new meta learning framework that effectively utilizes the
task-specific meta information such as local graph neighbors and reasoning
paths in KGs. Specifically, we design a meta-encoder that encodes the meta
information into task-specific initialization parameters for different tasks.
This allows our reasoning module to have diverse starting points when learning
to reason over different relations, which is expected to better fit the target
task. On two few-shot knowledge base completion benchmarks, we show that the
augmented task-specific meta-encoder yields much better initial point than MAML
and outperforms several few-shot learning baselines.
