  Knowledge graph simple question answering (KGSQA), in its standard form, does
not take into account that human-curated question answering training data only
cover a small subset of the relations that exist in a Knowledge Graph (KG), or
even worse, that new domains covering unseen and rather different to existing
domains relations are added to the KG. In this work, we study KGSQA in a
previously unstudied setting where new, unseen domains are added during test
time. In this setting, question-answer pairs of the new domain do not appear
during training, thus making the task more challenging. We propose a
data-centric domain adaptation framework that consists of a KGSQA system that
is applicable to new domains, and a sequence to sequence question generation
method that automatically generates question-answer pairs for the new domain.
Since the effectiveness of question generation for KGSQA can be restricted by
the limited lexical variety of the generated questions, we use distant
supervision to extract a set of keywords that express each relation of the
unseen domain and incorporate those in the question generation method.
Experimental results demonstrate that our framework significantly improves over
zero-shot baselines and is robust across domains.
