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Enhancing SPARQL Query Generation for Knowledge Base Question Answering Systems by Learning to Correct Triplets.
- Source :
- Applied Sciences (2076-3417); Feb2024, Vol. 14 Issue 4, p1521, 19p
- Publication Year :
- 2024
-
Abstract
- 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 errors (e.g., (subject, relation, object) is predicted as (object, relation, subject)). To address this limitation, we introduce TSET (Triplet Structure Enhanced T5), a model with a novel pretraining stage positioned between the initial T5 pretraining and the fine-tuning for the Text-to-SPARQL task. In this intermediary stage, we introduce a new objective called Triplet Structure Correction (TSC) to train the model on a SPARQL corpus derived from Wikidata. This objective aims to deepen the model's understanding of the order of triplets. After this specialized pretraining, the model undergoes fine-tuning for SPARQL query generation, augmenting its query-generation capabilities. We also propose a method named "semantic transformation" to fortify the model's grasp of SPARQL syntax and semantics without compromising the pre-trained weights of T5. Experimental results demonstrate that our proposed TSET outperforms existing methods on three well-established KBQA datasets: LC-QuAD 2.0, QALD-9 plus, and QALD-10, establishing a new state-of-the-art performance (95.0% F1 and 93.1% QM on LC-QuAD 2.0, 75.85% F1 and 61.76% QM on QALD-9 plus, 51.37% F1 and 40.05% QM on QALD-10). [ABSTRACT FROM AUTHOR]
- Subjects :
- QUESTION answering systems
KNOWLEDGE base
INSTRUCTIONAL systems
NATURAL languages
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 14
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 175652509
- Full Text :
- https://doi.org/10.3390/app14041521