  For large-scale knowledge graphs (KGs), recent research has been focusing on
the large proportion of infrequent relations which have been ignored by
previous studies. For example few-shot learning paradigm for relations has been
investigated. In this work, we further advocate that handling uncommon entities
is inevitable when dealing with infrequent relations. Therefore, we propose a
meta-learning framework that aims at handling infrequent relations with
few-shot learning and uncommon entities by using textual descriptions. We
design a novel model to better extract key information from textual
descriptions. Besides, we also develop a novel generative model in our
framework to enhance the performance by generating extra triplets during the
training stage. Experiments are conducted on two datasets from real-world KGs,
and the results show that our framework outperforms previous methods when
dealing with infrequent relations and their accompanying uncommon entities.
