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%.

提出了一种基于案例推理的方法，CBR-iKB，用于知识库问答系统，该方法可以适应不完整的知识库，通过新颖的非参数推理算法，综合多个推理链的决策，无需特定的训练或微调，达到了 100％的 MetaQA 准确度，同时在 WebQSP 下取得了 70％的准确率，超过了现有最优方法 22.3％以上。