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CySpider: A Neural Semantic Parsing Corpus with Baseline Models for Property Graphs

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AI 2023: Advances in Artificial Intelligence (AI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14472))

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Abstract

Enterprise knowledge graphs are gaining increasing popularity in industrial applications, with a pressing demand for natural language interfaces to support non-technical end-users. For natural language queries to relational databases, the neural semantic parsing task Text-to-SQL achieves strong performance in translating text inputs to SQL queries. However, very few public corpora are available for the training of neural semantic parsing models that convert textual queries to graph query languages. In this research, we develop a generic SQL2Cypher algorithm that can map a SQL query to a set of Cypher clauses, where Cypher is a query language used by a popular property graph database Neo4j. The converted Cypher statement is then combined with the original natural language query to create a parallel corpus that enables end-to-end training of neural semantic parsing models for Text-to-Cypher. To evaluate the dataset quality, we construct a corresponding graph database to obtain execution accuracy. In addition, the Text-to-Cypher corpus features four transformer-based baseline models. The availability of such corpus and baseline models is critical in developing and benchmarking new machine learning methods in advancing natural language interfaces for fact retrieval from large graph-based knowledge repositories. The source code and dataset are available at github(https://github.com/22842219/SemanticParser4Graph).

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Notes

  1. 1.

    https://db-engines.com/en/ranking.

  2. 2.

    https://github.com/jOOQ/jOOQ.

  3. 3.

    https://github.com/neo4j-contrib/sql2cypher.

  4. 4.

    https://github.com/mozilla/moz-sql-parser.

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Acknowledgment

This research is supported by the Australian Research Council through the Centre of Transforming Maintenance through Data Science (grant number IC180100030), funded by the Australian Government.

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Authors and Affiliations

  1. UWA NLP-TLP Group, 35 Stirling Hwy, Crawley, Perth, WA, 6009, Australia

    Ziyu Zhao, Wei Liu, Tim French & Michael Stewart

  2. School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, Australia

    Ziyu Zhao, Wei Liu, Tim French & Michael Stewart

Authors
  1. Ziyu Zhao
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  2. Wei Liu
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  3. Tim French
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  4. Michael Stewart
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Corresponding author

Correspondence to Ziyu Zhao .

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Editors and Affiliations

  1. The University of Sydney, Darlington, NSW, Australia

    Tongliang Liu

  2. Monash University, Clayton, VIC, Australia

    Geoff Webb

  3. The University of Newcastle, Callaghan, NSW, Australia

    Lin Yue

  4. CSIRO Data61, Sydney, NSW, Australia

    Dadong Wang

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Zhao, Z., Liu, W., French, T., Stewart, M. (2024). CySpider: A Neural Semantic Parsing Corpus with Baseline Models for Property Graphs. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14472. Springer, Singapore. https://doi.org/10.1007/978-981-99-8391-9_10

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  • DOI: https://doi.org/10.1007/978-981-99-8391-9_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8390-2

  • Online ISBN: 978-981-99-8391-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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