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Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Author

Listed:
  • Pin Ni

    (University College London)

  • Ramin Okhrati

    (University College London)

  • Steven Guan

    (Xi’an Jiaotong-Liverpool University)

  • Victor Chang

    (Aston University)

Abstract

Text-to-GraphQL (Text2GraphQL) is a task that converts the user's questions into Graph + QL (Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GraphQL task. This solution uses the Adapter pre-trained by “the linking of GraphQL schemas and the corresponding utterances” as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GraphQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GraphQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.

Suggested Citation

  • Pin Ni & Ramin Okhrati & Steven Guan & Victor Chang, 2024. "Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot," Information Systems Frontiers, Springer, vol. 26(1), pages 137-156, February.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:1:d:10.1007_s10796-022-10295-0
    DOI: 10.1007/s10796-022-10295-0
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    References listed on IDEAS

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    1. B. Golden & L. Bodin & T. Doyle & W. Stewart, 1980. "Approximate Traveling Salesman Algorithms," Operations Research, INFORMS, vol. 28(3-part-ii), pages 694-711, June.
    2. Pin Ni & Yuming Li & Victor Chang, 2020. "Research on Text Classification Based on Automatically Extracted Keywords," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 16(4), pages 1-16, October.
    3. Pin Ni & Yuming Li & Victor Chang, 2020. "Recommendation and Sentiment Analysis Based on Consumer Review and Rating," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 11(2), pages 11-27, July.
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