论文标题
CBR-ikb：一种基于案例的推理方法，用于回答不完整的知识基础
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
论文作者
论文摘要
知识库（KB）通常是不完整的，并且在实践中不断变化。但是，在许多问题回答应用程序以及知识库的问题中，KBS的稀疏性质经常被忽略。为此，我们提出了一种基于案例的推理方法CBR-IKB，用于知识库问题答案（KBQA），以不完整的KB为我们的主要重点。我们的方法与新的非参数推理算法从多个推理链中结合了决策。根据设计，CBR-IKB可以无缝地适应KB的变化，而无需任何特定任务的培训或微调。我们的方法在元数据上达到了100％的准确性，并在多个基准上建立了新的最先进。例如，在不完整的KB设置下，CBR-IKB在WebQSP上的精度为70％，表现优于现有的最新方法的准确性22.3％。
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%.
