Research on Identification and Evaluation Method of Medical Experts' Expertise Domains in Online Health Community Based on Knowledge Graph

Authors

• Yunjiang XI, School of Business Administration, South China University of Technology, Guangzhou 510641
• Qian ZHANG, School of Business Administration, South China University of Technology, Guangzhou 510641
• Man LI, School of Business Administration, South China University of Technology, Guangzhou 510641
• Juan YU, School of Economics and Management, Fuzhou University, Fuzhou 350108

Keywords

online health community; expert finding; knowledge graph; entity relation joint extraction; reliability

Abstract

[Purpose/significance] This study aims to identify the expertise domains of medical experts, and evaluates their domain levels to provide a basis for community expert recommendation.

[Method/process] This study utilized the improved OneRel model to structure community historical Q&A into entity relation triples. Then used the knowledge graph triples to test the consistency between the medical knowledge in the community Q&A and the domain knowledge, and finally obtained the doctor's domain levels by aggregating in each expertise domain.

[Result/conclusion] Using the data example from xywy.com website, 214 doctors in the community were ranked in terms of their average level of expertise domains and their respective reliable domains levels were identified. The study verified that the basic information self-reported by doctors in online health communities, such as their expertise and personal profiles, did not fully correspond to their real competence characteristics. Compared with other medical expert discovery methods, this method can intelligently identify and evaluate doctors' expertise domains, demonstrating objectivity, accuracy, and strong interpretability.

First Page

24

Last Page

34

Submission Date

February 2025

Revision Date

March 2025

Acceptance Date

March 2025

Publication Date

October 2025

Digital Object Identifier (DOI)

ꎺ 10.19809/j.cnki.kjqbyj.2025.04.003

Recommended Citation

XI, Yunjiang; ZHANG, Qian; LI, Man; and YU, Juan (2025) "Research on Identification and Evaluation Method of Medical Experts' Expertise Domains in Online Health Community Based on Knowledge Graph," Journal of Scientific Information Research: Vol. 7: Iss. 4, Article 3.
DOI: ꎺ 10.19809/j.cnki.kjqbyj.2025.04.003
Available at: https://eng.kjqbyj.com/journal/vol7/iss4/3

Reference

[1] 韩普, 叶东宇. 基于语义增强的在线健康社区情感分析研究[J]. 科技情报研究, 2024, 6 (2): 88-99.
[2] 杜刚, 韩召. 组态视角下患者在线问诊选择的驱动因素[J]. 系统管理学报, 2024, 33 (5): 1270-1283.
[3] 金燕, 胡香, 杨瑞仙, 等. 在线健康平台的信息质量治理现状及提升策略研究[J]. 现代情报, 2023, 43 (8): 79-90.
[4] YE Y, ZHAO Y, SHANG J, et al. A hybrid IT framework for identifying high-quality physicians using big data analytics[J]. International Journal of Information Management, 2019, 47: 65-75.
[5] 孟秋晴, 熊回香, 童兆莉, 等. 基于在线问诊文本信息的医生标签自动生成研究[J]. 情报科学, 2020, 38 (5): 58-64, 72.
[6] 潘有能, 倪秀丽. 基于Labeled-LDA模型的在线医疗专家推荐研究[J]. 数据分析与知识发现, 2020, 4 (4): 34-43.
[7] KOU X C. Personalized doctor recommendation method based on patient online consultation text[C]//Proceedings of the 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) . Fuzhou, IEEE, 2021: 31-34.
[8] LU X X, ZHANG Y B, LI J, et al. Doctor recommendation in online health forums via expertise learning[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Dublin, 2022: 1111-1123.
[9] 聂卉, 蔡瑞昇. 引入注意力机制的在线问诊推荐研究[J]. 数据分析与知识发现, 2023, 7 (8): 138-148.
[10] HE T Z, GUO C L, CHU Y F. Enhanced user interest and expertise modeling for expert recommendation[C]//Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR) . Online, IEEE, 2021: 556-562.
[11] 杜军威, 邹树林, 李浩杰, 等. 基于问答语义匹配的知识社区新问题专家推荐方法[J]. 电子学报, 2023, 51 (7): 1875-1888.
[12] PATIL S, LEE K. Detecting experts on Quora: by their activity, quality of answers, linguistic characteristics and temporal behaviors[J]. Social Network Analysis and Mining, 2016, 6 (1): 1-11.
[13] LE L T, SHAH C. Retrieving rising stars in focused community question-answering[C]//Proceedings of the Intelligent Information and Database Systems: 8th Asian Conference (ACIIDS) . Springer, 2016: 25-36.
[14] MOMTAZI S, NAUMANN F. Topic modeling for expert finding using Latent Dirichlet Allocation[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2013, 3 (5): 346-353.
[15] CHI R J, WU B, WANG L. Expert identification based on dynamic LDA topic model[C]//Proceedings of the 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) . Guangzhou, IEEE, 2018: 881-888.
[16] WANG G A, JIAO J, ABRAHAMS A S, et al. ExpertRank: A topic-aware expert finding algorithm for online knowledge communities[J]. Decision Support Systems, 2013, 54 (3): 1442-1451.
[17] LI H, LI J J, LI G H, et al. Expert recommendation in community question answering via heterogeneous content network embedding[J]. Computers, Materials & Continua, 2023, 75 (1): 1687-1709.
[18] YE G H, LI S Y, WU L Q, et al. Multi-dimensional feature fusion-based expert recommendation in community question answering[J]. The Electronic Library, 2024, 42 (6): 996-1016.
[19] 路薇, 高盼, 翟运开. 带有反馈调节的远程医疗专家自适化推荐[J]. 系统管理学报, 2023, 32 (5): 960-975.
[20] ZHENG Z, QIU Z P, XIONG H, et al. DDR: Dialogue Based Doctor Recommendation for Online Medical Service[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Washington, 2022: 4592-4600.
[21] QIAN L F, WANG J, LIN H F, et al. Multi-hop interactive attention based classification network for expert recommendation[J]. Neurocomputing, 2022, 488 (C): 436-443.
[22] 李明珠, 米传民, 苟小义, 等. 考虑主题兴趣和领域权威的问答社区专家推荐研究[J]. 数据分析与知识发现, 2024, 8 (5): 68-79.
[23] SAHEB T, DEHGHANI M, SAHEB T. Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis[J]. Sustainable Computing: Informatics and Systems, 2022, 35: 100699.
[24] DARAZ L, MORROW A S, PONCE O J, et al. Can patients trust online health information? A meta-narrative systematic review addressing the quality of health information on the internet[J]. Journal of General Internal Medicine, 2019, 34 (9): 1884-1891.
[25] STACKS D W, SALWEN M B. An integrated approach to communication theory and research[M]. New York: Lawrence Erlbaum Associate, 1996.
[26] HOVLAND C I, JANIS I L, KELLEY H H. Communication and persuasion[M]. New Haven, CT, US: Yale University Press, 1953.
[27] SINGHAL A. Introducing the Knowledge Graph: things, not strings[EB/OL]. (2012-05-16) [2024-05-10]. https://blog.google/products/search/introducing-knowledge-graph-things-not.
[28] 黄贺瑄, 王晓燕, 顾正位, 等. 医学知识图谱构建技术及发展现状研究[J]. 计算机工程与应用, 2023, 59 (13): 33-48.
[29] SHANG Y M, HUANG H Y, MAO X L. OneRel: Joint Cntity and Relation Extraction with One Module in One Step[J]. Computation and Language, 2022, 36 (10): 11285-11293.
[30] 张仰森, 刘帅康, 刘洋, 等. 基于深度学习的实体关系联合抽取研究综述[J]. 电子学报, 2023, 51 (4): 1093-1116.
[31] CHINCHOR N, MARSH E. MUC-7 information extraction task definition[C]//Proceedings of the 7th Message Understanding Conf. Fairfax, Virginia, 1998: 359-367.
[32] LUO L, YANG Z H, YANG P, et al. An attention-based BILSTM-CRF approach to document-level chemical named entity recognition[J]. Bioinformatics, 2018, 34 (8): 1381-1388.
[33] ZHENG S C, WANG F, BAO H Y, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, 2017: 1227-1236.
[34] WANG Q, DAI S T, XU B F, et al. Building Chinese biomedical language models via multi-level text discrimination[EB/OL]. (2022-03-02) [2024-05-10]. https://arxiv.org/abs/2110.07244.
[35] ZHANG Z S, ZHANG H Q, CHEN K M, et al. Mengzi: Towards lightweight yet ingenious pre-trained models for Chinese[J]. (2021-10-14) [2024-05-10]. https://arxiv.org/abs/2110.06696.