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

CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases