  When answering natural language questions over knowledge bases, missing
facts, incomplete schema and limited scope naturally lead to many questions
being unanswerable. While answerability has been explored in other QA settings,
it has not been studied for QA over knowledge bases (KBQA). We create
GrailQAbility, a new benchmark KBQA dataset with unanswerability, by first
identifying various forms of KB incompleteness that make questions
unanswerable, and then systematically adapting GrailQA (a popular KBQA dataset
with only answerable questions). Experimenting with three state-of-the-art KBQA
models, we find that all three models suffer a drop in performance even after
suitable adaptation for unanswerable questions. In addition, these often detect
unanswerability for wrong reasons and find specific forms of unanswerability
particularly difficult to handle. This underscores the need for further
research in making KBQA systems robust to unanswerability
