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

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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Bibliographic Details
Main Authors: Jiexing Qi, Chang Su, Zhixin Guo, Lyuwen Wu, Zanwei Shen, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Applied Sciences
Subjects:
Knowledge Base Question Answering
Text-to-SPARQL
semantic parsing
further pretraining
Triplet Structure
Online Access:https://www.mdpi.com/2076-3417/14/4/1521

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