Existing KBQA methods have traditionally relied on multi-stage methodologies, involving tasks such as entity linking, subgraph retrieval and query structure generation. However, multi-stage approaches are dependent on the accuracy of preceding steps, leading to cascading errors and increased inference time. Although a few studies have explored the use of end-to-end models, they often suffer from lower accuracy and generate inoperative query that is not supported by the underlying data. Furthermore, most prior approaches are limited to the static training data, potentially overlooking the evolving nature of knowledge bases over time. To address these challenges, we present a novel end-to-end natural language to SPARQL framework, SPARKLE. Notably SPARKLE leverages the structure of knowledge base directly during the decoding, effectively integrating knowledge into the query generation. Our study reveals that simply referencing knowledge base during inference significantly reduces the occurrence of inexecutable query generations. SPARKLE achieves new state-of-the-art results on SimpleQuestions-Wiki and highest F1 score on LCQuAD 1.0 (among models not using gold entities), while getting slightly lower result on the WebQSP dataset. Finally, we demonstrate SPARKLE's fast inference speed and its ability to adapt when the knowledge base differs between the training and inference stages.

我们提出了一种新颖的端到端自然语言到SPARQL框架SPARKLE，它在解码过程中直接利用知识库的结构，从而有效地将知识整合到查询生成中。通过引用知识库简化查询生成，SPARKLE在SimpleQuestions-Wiki数据集上取得了最新的最好结果，并在LCQuAD 1.0上获得最高的F1分数（不使用黄金实体模型），同时在WebQSP数据集上稍微降低了结果。此外，我们展示了SPARKLE的快速推理速度和在训练和推理阶段知识库不同的情况下适应的能力。

SPARKLE: 在解码过程中增强SPARQL生成的直接知识图谱集成