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LLM-Based SPARQL Generation with Selected Schema from Large Scale Knowledge Base

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Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence (CCKS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1923))

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Abstract

Knowledge base question answering (KBQA) aims to answer natural language questions using structured knowledge bases. Common approaches include semantic parsing-based approaches and retrieval-based approaches. However, both approaches have some limitations. Retrieval-based methods struggle with complex reasoning requirements. Semantic parsing approaches have a complex reasoning process and cannot tolerate errors in earlier steps when generating the final logical form. In this paper, we proposed a large language model (LLM)-based SPARQL generation model, which accepts multiple candidate entities and relations as inputs, reducing the reliance on mention extraction and entity linking performance, and we found an entity combination strategy based on mentions, which can produce multiple SPARQL queries for a single question to boost the chances of finding the correct answer. Finally, our model achieves state-of-the-art performance in the CCKS2023 CKBQA competition, F1 score is 75.63%.

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Notes

  1. 1.

    https://github.com/THUDM/ChatGLM-6B.

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Authors and Affiliations

  1. Lenovo Knowdee (Beijing) Intelligent Technology, Haidian District, Beijing, China

    Shuangtao Yang, Mao Teng, Xiaozheng Dong & Fu Bo

Authors
  1. Shuangtao Yang
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  2. Mao Teng
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  3. Xiaozheng Dong
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  4. Fu Bo
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Corresponding author

Correspondence to Shuangtao Yang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    Haofen Wang

  2. Chinese Academy of Sciences, Beijing, China

    Xianpei Han

  3. Harbin Institute of Technology, Harbin, China

    Ming Liu

  4. Nanjing University, Nanjing, China

    Gong Cheng

  5. University of South China, Hengyang, China

    Yongbin Liu

  6. Zhejiang University, Hangzhou, China

    Ningyu Zhang

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Yang, S., Teng, M., Dong, X., Bo, F. (2023). LLM-Based SPARQL Generation with Selected Schema from Large Scale Knowledge Base. In: Wang, H., Han, X., Liu, M., Cheng, G., Liu, Y., Zhang, N. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers Artificial General Intelligence. CCKS 2023. Communications in Computer and Information Science, vol 1923. Springer, Singapore. https://doi.org/10.1007/978-981-99-7224-1_24

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  • DOI: https://doi.org/10.1007/978-981-99-7224-1_24

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  • Print ISBN: 978-981-99-7223-4

  • Online ISBN: 978-981-99-7224-1

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