  Knowledge bases (KBs) are often incomplete and constantly changing in
practice. Yet, in many question answering applications coupled with knowledge
bases, the sparse nature of KBs is often overlooked. To this end, we propose a
case-based reasoning approach, CBR-iKB, for knowledge base question answering
(KBQA) with incomplete-KB as our main focus. Our method ensembles decisions
from multiple reasoning chains with a novel nonparametric reasoning algorithm.
By design, CBR-iKB can seamlessly adapt to changes in KBs without any
task-specific training or fine-tuning. Our method achieves 100% accuracy on
MetaQA and establishes new state-of-the-art on multiple benchmarks. For
instance, CBR-iKB achieves an accuracy of 70% on WebQSP under the incomplete-KB
setting, outperforming the existing state-of-the-art method by 22.3%.
