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. 2023 Jul 13;23:119. doi: 10.1186/s12911-023-02230-3

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — System interface and information processing flow

The user can enter a natural language question directly in the text field or press the voice recognition button and speak the question using voice recognition. The input question is first analyzed by morphological analysis and then converted to synonyms based on the thesaurus. Then, Watson’s NLC has two steps: The first step, “Category-sorting NLC,” selects the category that best matches the entered question among six categories. After synonym conversion, the questions are re-entered in the “Category NLC” determined in the “Category-sorting NLC” to get an answer