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Research paper | Open Access

A study of similar question retrieval method in online health communities

Bufei Xing1, Haonan Yin2, Zhijun Yan3( ), Jiachen Wang3
Baidu Online Network Technology Beijing Co., Ltd, Beijing, China
Paul Merage School of Business, University of California, Irvine, California, USA
School of Management and Economics, Beijing Institute of Technology, Beijing, China
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Abstract

Purpose

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

Design/methodology/approach

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

Findings

The experiment results show that the proposed method outperforms the baseline methods.

Originality/value

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

Keywords

Topic modelOnline health communitiesQuestion retrievalNamed entity extraction

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International Journal of Crowd Science
Volume 5 Issue 2,
August 2021
Pages 154-165
DOI: 10.1108/IJCS-03-2021-0006
Cite this article:
Xing B, Yin H, Yan Z, et al. A study of similar question retrieval method in online health communities. International Journal of Crowd Science, 2021, 5(2): 154-165. https://doi.org/10.1108/IJCS-03-2021-0006

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Received: 02 March 2021
Revised: 19 April 2021
Accepted: 22 April 2021
Published: 21 June 2021
© The author(s)

Bufei Xing, Haonan Yin, Zhijun Yan and Jiachen Wang. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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