Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets

Generating SPARQL queries from natural language questions is challenging in Knowledge Base Question Answering (KBQA) systems. The current state-of-the-art models heavily rely on fine-tuning pretrained models such as T5. However, these methods still encounter critical issues such as triple-flip error...

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Главные авторы:	Jiexing Qi, Chang Su, Zhixin Guo, Lyuwen Wu, Zanwei Shen, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Формат:	Artigo
Язык:	Inglês
Опубликовано:	MDPI AG 2024-02-01
Серии:	Applied Sciences
Предметы:	Knowledge Base Question Answering Text-to-SPARQL semantic parsing further pretraining Triplet Structure
Online-ссылка:	https://www.mdpi.com/2076-3417/14/4/1521
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