  Knowledge Graph Completion (KGC) has been proposed to improve Knowledge
Graphs by filling in missing connections via link prediction or relation
extraction. One of the main difficulties for KGC is a low resource problem.
Previous approaches assume sufficient training triples to learn versatile
vectors for entities and relations, or a satisfactory number of labeled
sentences to train a competent relation extraction model. However, low resource
relations are very common in KGs, and those newly added relations often do not
have many known samples for training. In this work, we aim at predicting new
facts under a challenging setting where only limited training instances are
available. We propose a general framework called Weighted Relation Adversarial
Network, which utilizes an adversarial procedure to help adapt
knowledge/features learned from high resource relations to different but
related low resource relations. Specifically, the framework takes advantage of
a relation discriminator to distinguish between samples from different
relations, and help learn relation-invariant features more transferable from
source relations to target relations. Experimental results show that the
proposed approach outperforms previous methods regarding low resource settings
for both link prediction and relation extraction.
