  In the past years, Knowledge-Based Question Answering (KBQA), which aims to
answer natural language questions using facts in a knowledge base, has been
well developed. Existing approaches often assume a static knowledge base.
However, the knowledge is evolving over time in the real world. If we directly
apply a fine-tuning strategy on an evolving knowledge base, it will suffer from
a serious catastrophic forgetting problem. In this paper, we propose a new
incremental KBQA learning framework that can progressively expand learning
capacity as humans do. Specifically, it comprises a margin-distilled loss and a
collaborative exemplar selection method, to overcome the catastrophic
forgetting problem by taking advantage of knowledge distillation. We reorganize
the SimpleQuestion dataset to evaluate the proposed incremental learning
solution to KBQA. The comprehensive experiments demonstrate its effectiveness
and efficiency when working with the evolving knowledge base.
