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.
Highlights
The fast popularity of Web 2.0 results in a great change in health area and leads to the emergence of Medicine 2.0 (Eysenbach, 2008; Van De Belt et al, 2010), which provides an interactive and effective communication platform for doctors and patients
More and more patients like to find health information and share their experience in online health communities (OHC), which is widely adopted in Medicine 2.0
We propose a novel approach based on distributional semantic vectors to compute domain knowledge similarity of health questions
Summary
The fast popularity of Web 2.0 results in a great change in health area and leads to the emergence of Medicine 2.0 (Eysenbach, 2008; Van De Belt et al, 2010), which provides an interactive and effective communication platform for doctors and patients. More and more patients like to find health information and share their experience in online health communities (OHC), which is widely adopted in Medicine 2.0 International Journal of Crowd Science Vol 5 No 2, 2021 pp. Published in International Journal of Crowd Science. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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