Consultation recommendation method based on knowledge graph and dialogue structure

doi:10.11772/j.issn.1001-9081.2024050573

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (4): 1157-1168.DOI: 10.11772/j.issn.1001-9081.2024050573

• Artificial intelligence • Previous Articles Next Articles

Consultation recommendation method based on knowledge graph and dialogue structure

Chun XU, Shuangyan JI(), Huan MA, Enwei SUN, Mengmeng WANG, Mingyu SU

School of Information Management，Xinjiang University of Finance and Economics，Urumqi Xinjiang 830012，China

Received:2024-05-09 Revised:2024-07-09 Accepted:2024-07-11 Online:2024-07-25 Published:2025-04-10
Contact: Shuangyan JI
About author:XU Chun， born in 1977， Ph. D.， professor. Her research interests include natural language processing， big data analysis.
JI Shuangyan， born in 1999， M. S. candidate. Her research interests include natural language processing， medical data analysis.
MA Huan， born in 1980， M. S.， lecturer. Her research interests include natural language processing， medical data analysis.
SUN Enwei， born in 1998， M. S. candidate. His research interests include natural language processing， medical data analysis.
WANG Mengmeng， born in 1998， M. S. candidate. Her research interests include natural language processing， big data analysis.
SU Mingyu， born in 2000， M. S. candidate. Her research interests include natural language processing， big data analysis.
Supported by:
National Natural Science Foundation of China(62266041);Natural Science Foundation of Xinjiang(2023D01A73)

基于知识图谱和对话结构的问诊推荐方法

徐春, 吉双焱(), 马欢, 孙恩威, 王萌萌, 苏明钰

新疆财经大学信息管理学院，乌鲁木齐 830012

通讯作者: 吉双焱
作者简介:徐春（1977—），女，贵州毕节人，教授，博士，CCF会员，主要研究方向：自然语言处理、大数据分析
吉双焱（1999—），女，山西运城人，硕士研究生，主要研究方向：自然语言处理、医疗数据分析
马欢（1980—），女（回族），青海西宁人，讲师，硕士，主要研究方向：自然语言处理、医疗数据分析
孙恩威（1998—），男，安徽马鞍山人，硕士研究生，主要研究方向：自然语言处理、医疗数据分析
王萌萌（1998—），女，四川南充人，硕士研究生，主要研究方向：自然语言处理、大数据分析
苏明钰（2000—），女，辽宁鞍山人，硕士研究生，主要研究方向：自然语言处理、大数据分析。
基金资助:
国家自然科学基金资助项目(62266041);新疆自然科学基金资助项目(2023D01A73)

Abstract

Abstract:

Aiming at the problems that the existing consultation recommendation methods do not fully utilize the rich dialogue information between doctors and patients and do not capture patients’ real-time health needs and preferences， a consultation recommendation method based on Knowledge Graph and Dialogue Structure （KGDS） was proposed. Firstly， a medical Knowledge Graph （KG） including comment sentiment analysis and professional medical knowledge was constructed to improve the fine-grained feature representations of doctors and patients. Secondly， in the patient representation learning part， a patient query encoder was designed to extract key features of query text at both word and sentence levels， and to improve the higher-level feature interactions between doctor and patient vectors through attention mechanism. Thirdly， a diagnosis dialogue was modeled to make full use of the rich dialogue information between doctors and patients to enhance the doctor-patient feature representation. Finally， a dialogue simulator based on contrastive learning was designed to capture the dynamic needs and real-time preferences of patients， and the simulated dialogue representation was used to support recommendation score prediction. Experimental results on a real dataset show that compared with the optimal baseline method， KGDS increases AUC（Area Under the Curve）， MRR@15（Mean Reciprocal Rank）， Diversity@15， F1@15， HR@15 （Hit Ratio） and NDCG@15（Normalized Discounted Cumulative Gain） by 1.82， 1.78， 3.85， 3.06， 10.02 and 4.51 percentage points， respectively， which verifies the effectiveness of the proposed consultation recommendation method， and it can be seen that adding sentiment analysis and KG improves the interpretability of the recommendation results.

Key words: Knowledge Graph (KG), dialogue structure, consultation recommendation, comment sentiment analysis, attention mechanism, interpretability

摘要：

针对现有的问诊推荐方法未能充分利用医患间丰富的对话信息和无法捕捉患者实时的健康需求和偏好的问题，提出一种基于知识图谱和对话结构的问诊推荐方法（KGDS）。首先，构建包含评论情感分析和医学专业知识的医疗知识图谱（KG），增强医生和患者的细粒度特征表示；其次，在患者表示学习部分，设计一种患者查询编码器，从词级和句级这2个层面提取查询文本的关键特征，并通过注意力机制加强医患向量间的高阶特征交互；再次，建模诊断对话，充分利用医患间丰富的对话信息增强医患特征表示；最后，设计基于对比学习的对话模拟器，捕捉患者的动态需求和实时偏好，利用模拟的对话表示辅助推荐得分的预测。在真实数据集上的实验结果表明，KGDS相较于最优基线方法在曲线下面积（AUC）、平均值倒数秩（MRR@15）、推荐多样性（Diversity@15）、调和平均值（F1@15）、命中率（HR@15）和归一化折损累计增益（NDCG@15）上分别提高了1.82、1.78、3.85、3.06、10.02和4.51个百分点，验证了KGDS的有效性，且可见情感分析和KG的纳入增强了推荐结果的可解释性。

关键词: 知识图谱, 对话结构, 问诊推荐, 评论情感分析, 注意力机制, 可解释性

CLC Number:

TP391

Chun XU, Shuangyan JI, Huan MA, Enwei SUN, Mengmeng WANG, Mingyu SU. Consultation recommendation method based on knowledge graph and dialogue structure[J]. Journal of Computer Applications, 2025, 45(4): 1157-1168.

徐春, 吉双焱, 马欢, 孙恩威, 王萌萌, 苏明钰. 基于知识图谱和对话结构的问诊推荐方法[J]. 《计算机应用》唯一官方网站, 2025, 45(4): 1157-1168.

Figures/Tables 19

Fig. 1 Example of doctor Hu’s information representation in KG

Tab. 1 Description of KG dataset

类型	样本总数	属性
医生	2 956	性别、所属医院、职称、科室、擅长疾病、诊断患者数、方面级情感得分
医院	238	位置、等级、类型、全国排名、省级排名、患者评分、全国排名前10的科室
实体	27 628	医生及其属性、医院及其属性、疾病、症状、药物等
关系	135	拥有并发症、病因、科室、治疗药物等
三元组	65 819	（头实体，关系，尾实体）

Tab. 2 Symbol description

符号	描述	符号	描述
$Θ$	模型参数集	$H u$	医生u的历史诊断对话
$p$	患者p的个人资料向量	$n$	对话中的语句数
$u$	医生u的个人资料向量	$m$	医生诊断历史的长度
$T i$	第i个话语	$z i$	第i个语句的角色
$D i$	第i个诊断对话	$w i$	第i个词
$Q p$	患者p的查询

Tab. 2 Symbol description

符号	描述	符号	描述
$Θ$	模型参数集	$H u$	医生u的历史诊断对话
$p$	患者p的个人资料向量	$n$	对话中的语句数
$u$	医生u的个人资料向量	$m$	医生诊断历史的长度
$T i$	第i个话语	$z i$	第i个语句的角色
$D i$	第i个诊断对话	$w i$	第i个词
$Q p$	患者p的查询

Fig. 2 Framework of KGDS model

Fig. 3 Dialogue encoder

Tab. 3 Statistics of data

数据类型/来源	样本数	数据类型/来源	样本数
医生	2 956	历史数据集	38 166
患者	68 376	训练集	38 395
正、负样本总数	95 705	验证集	9 571
正样本	67 192	测试集	9 573
负样本	28 513

Tab. 4 Model parameters

模块	Input	Encoder
患者表示学习	个人资料编码器：C×64	Liner：64→128
	查询编码器‒词级： C×30×64	KG嵌入：30×64→30×128 Attention：30×128→128， d=128，h=2
	句级特征编码器： C×30×128	Transformer Multi-head self-attention：128→128
	融合层：C×256	Liner：256→128
医生表示学习	个人资料编码：C×64	TransD嵌入：64→128
医生表示学习	对话编码器： C×15×10×128	Multi-head self-attention： 15×10×128→15×128，h=2 前馈网络：Flatten； Linear：1 920→128
对话模拟	C×128	MLP Linear：128→128
预测层	C×384	线性层：384→128→C

Tab. 5 Comparison of experimental results of different methods

方法	AUC	MRR@15	Diversity@15	F1@15
DRGAN	0.842 7	0.122 4	0.819 0	0.621 0
PMF-CNN	0.841 5	0.136 7	0.822 8	0.629 3
文献［39］方法	0.892 4	0.198 3	0.864 3	0.675 6
MUL-ATT	0.886 9	0.189 6	0.853 7	0.667 2
KGAT	0.869 5	0.165 7	0.832 5	0.630 8
文献［39］方法-KG	0.872 1	0.185 4	0.851 9	0.641 3
MUL-ATT-DS	0.869 3	0.180 2	0.846 3	0.655 7
KGDS-KG	0.887 9	0.198 7	0.867 2	0.669 4
KGDS-DS	0.891 2	0.205 3	0.880 1	0.684 1
KGDS	0.910 6	0.216 1	0.902 8	0.706 2

Fig. 4 Comparison of different methods on HR@K and NDCG@K

Tab. 6 Ablation experimental results

方法	AUC	MRR@15	Diversity@15	F1@15
KGDS-KG	0.887 9	0.198 7	0.867 2	0.669 4
KGDS-ABSA	0.904 5	0.208 7	0.879 6	0.688 3
KGDS-DS	0.891 2	0.205 3	0.880 1	0.684 1
KGDS-GRU	0.906 4	0.210 3	0.892 6	0.698 7
KGDS-Con	0.905 8	0.209 2	0.885 3	0.694 5
KGDS	0.910 6	0.216 1	0.902 8	0.706 2

Fig. 5 Comparison of HR@K and NDCG@K in ablation experiments

Tab. 7 Experimental results of different KG quality

KGs/%	AUC	MRR@15	Diversity@15	F1@15	HR@5	HR@10	HR@15	NDCG@5	NDCG@10	NDCG@15
‒30	0.859 0	0.171 2	0.839 2	0.653 8	0.392 7	0.558 1	0.647 9	0.251 3	0.324 8	0.337 6
‒20	0.883 2	0.183 8	0.867 8	0.669 0	0.425 6	0.579 4	0.699 7	0.272 4	0.358 7	0.364 0
‒10	0.892 8	0.205 1	0.891 4	0.686 3	0.445 2	0.606 7	0.721 6	0.297 3	0.364 1	0.382 6
0	0.910 6	0.212 6	0.902 8	0.706 2	0.463 8	0.621 0	0.753 4	0.302 5	0.371 2	0.398 8
10	0.857 3	0.184 1	0.880 5	0.673 6	0.413 6	0.568 0	0.694 7	0.258 9	0.326 7	0.331 2
20	0.823 1	0.132 8	0.835 7	0.639 4	0.365 7	0.455 0	0.581 6	0.204 8	0.241 7	0.259 3
30	0.791 1	0.106 8	0.801 1	0.618 9	0.305 2	0.391 7	9.474 8	0.173 6	0.208 8	0.225 4

Fig. 6 Influence of different parameters on KGDS performance

Tab. 8 Analysis of model complexity

方法	时间复杂度	参数量/10⁶
DRGAN	O（nd² ）	31.05
PMF-CNN	O（n²d+nd² ）	30.73
文献［39］方法	O（nd² ）	32.76
MUL-ATT	O（nd² ）	33.18
KGAT	O（n²d）	30.24
KGDS	O（n²d+nd² ）	32.26

Fig. 7 Training results of HR@15、NDCG@15 and Loss

Tab. 9 Example of online consultation

实体

特征和特征值

患者

性别：男，年龄：28岁，身高：181，体重：76，

咨询文本：近两天咽痛且鼻塞

胡医生

性别：男，所属医院：上海六院，职称：主任医师，

科室：耳喉鼻科，精通疾病：咽炎、鼻窦炎、

扁桃体肥大等，诊断患者数：5 183

上海六院

位置：上海市徐汇区，等级：三甲，类型：综合医院，

全国排名：32，区域排名：8，患者评分：4.8，

全国排名前十科室：骨科、耳鼻喉科、内分泌科

Fig. 8 Entity and relationship visualization

Tab. 10 Relevance scores of doctor Hu and relevant entities

实体	相关性得分	实体	相关性得分
咽喉肿痛	1.35	鼻塞	1.12
耳喉鼻	1.69	抗病毒口服液	0.54

Fig. 9 Heatmap of attention weight

References 41

1	ZHANG X， LIU S. Understanding relationship commitment and continuous knowledge sharing in online health communities： a social exchange perspective［J］. Journal of Knowledge Management， 2022， 26（3）： 592-614.
2	中国互联网络信息中心. 53次中国互联网络发展状况统计报告［R/OL］. ［2024-06-05］. .
	China Internet Network Information Center. The 53th statistical report on China’s Internet development［R/OL］. ［2024-06-05］. .
3	LU J， WU D， MAO M， et al. Recommender system application developments： a survey［J］. Decision Support Systems， 2015， 74： 12-32.
4	ZHOU Q， SU L， WU L， et al. Deep personalized medical recommendations based on the integration of rating features and review sentiment analysis［J］. Wireless Communications and Mobile Computing， 2021， 2021： No.5551318.
5	WU J， ZHANG G， XING Y， et al. A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors［J］. Applied Intelligence， 2023， 53（16）： 19093-19114.
6	XU C， WANG J， ZHU L， et al. PPMR： a privacy-preserving online medical service recommendation scheme in eHealthcare system［J］. IEEE Internet of Things Journal， 2019， 6（3）： 5665-5673.
7	CHEN J， YU J， LU W， et al. IR-Rec： an interpretive rules-guided recommendation over knowledge graph［J］. Information Sciences， 2021， 563： 326-341.
8	WU C， LIU S， ZENG Z， et al. Knowledge graph-based multi-context-aware recommendation algorithm［J］. Information Sciences， 2022， 595： 179-194.
9	ZHAO N， LONG Z， WANG J， et al. AGRE： a knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder［J］. Knowledge-Based Systems， 2023， 259： No.110078.
10	WANG H， ZHANG F， XIE X， et al. DKN： deep knowledge-aware network for news recommendation［C］// Proceedings of the 2018 World Wide Web Conference on World Wide Web. Republic and Canton of Geneva： International World Wide Web Conferences Steering Committee， 2018： 1835-1844.
11	郭洁，林佳瑜，梁祖红，等. 基于知识感知和跨层次对比学习的推荐方法［J］. 计算机应用， 2024， 44（4）：1121-1127.
	GUO J， LIN J Y， LIANG Z H， et al. Recommendation method based on knowledge-awareness and cross-level contrastive learning［J］. Journal of Computer Applications， 2024， 44（4）： 1121-1127.
12	GUO X， LIN W， LI Y， et al. DKEN： deep knowledge-enhanced network for recommender systems［J］. Information Sciences， 2020， 540： 263-277.
13	SHU H， HUANG J. Multi-task feature and structure learning for user-preference based knowledge-aware recommendation［J］. Neurocomputing， 2023， 532： 43-55.
14	樊海玮，鲁芯丝雨，张丽苗，等. 融合知识图谱和图注意力网络的引文推荐算法［J］. 计算机应用， 2023， 43（8）：2420-2425.
	FAN H W， LU X S Y， ZHANG L M， et al. Citation recommendation algorithm fusing knowledge graph and graph attention network［J］. Journal of Computer Applications， 2023， 43（8）： 2420-2425.
15	WANG H， ZHAO M， XIE X， et al. Knowledge graph convolutional networks for recommender systems［C］// Proceedings of the 2019 World Wide Web Conference. New York： ACM， 2019： 3307-3313.
16	JI G， HE S， XU L， et al. Knowledge graph embedding via dynamic mapping matrix［C］// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing （Volume 1： Long Papers）. Stroudsburg： ACL， 2015： 687-696.
17	赵梦媛，黄晓雯，桑基韬，等. 对话推荐算法研究综述［J］. 软件学报， 2021， 33（12）： 4616-4643.
	ZHAO M Y， HUANG X W， SANG J T， et al. Survey on conversational recommendation algorithms［J］. Journal of Software， 2021， 33（12）： 4616-4643.
18	RICH E. User modeling via stereotypes［J］. Cognitive Science， 1979， 3（4）： 329-354.
19	TOU F N， WILLIAMS M D， FIKES R， et al. RABBIT： an intelligent database assistant［C］// Proceedings of the 2nd AAAI Conference on Artificial Intelligence. Menlo Park： AAAI Press， 1982： 314-318.
20	HAMMOND K J， BURKE R D， LYTINEN S L. A case-based approach to knowledge navigation［C］// Proceedings of the 14th AAAI Workshop on Knowledge Discovery in Databases. Menlo Park： AAAI Press， 1994： 383-394.
21	CHRISTAKOPOULOU K， RADLINSKI F， HOFMANN K. Towards conversational recommender systems［C］// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2016： 815-824.
22	杨智强，殷钊，王衡. 结合用户交互行为和资源内容的资源推荐［J］. 计算机辅助设计与图形学学报， 2014， 26（5）：747-754.
	YANG Z Q， YIN Z， WANG H. Providing resource recommendation based on interactive behavior and resource content［J］. Journal of Computer-Aided Design and Computer Graphics， 2014， 26（5）： 747-754.
23	VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2017： 6000-6010.
24	XU B， WANG X， YANG B， et al. Target embedding and position attention with LSTM for aspect based sentiment analysis［C］// Proceedings of the 5th International Conference on Mathematics and Artificial Intelligence. New York： ACM， 2020： 93-97.
25	PORIA S， CAMBRIA E， KU L W， et al. A rule-based approach to aspect extraction from product reviews［C］// Proceedings of the 2nd Workshop on Natural Language Processing for Social Media. Stroudsburg： ACL， 2014： 28-37.
26	WANG B， WANG H. Bootstrapping both product features and opinion words from Chinese customer reviews with cross-inducing［C］// Proceedings of the 3rd International Joint Conference on Natural Language Processing： Volume-I. ［S.l.］： Asian Federation of Natural Language Processing， 2008： 289-295.
27	SCHOUTEN K， FRASINCAR F. Survey on aspect-level sentiment analysis［J］. IEEE Transactions on Knowledge and Data Engineering， 2016， 28（3）： 813-830.
28	LI X， LUO Y， WANG H， et al. Doctor selection based on aspect-based sentiment analysis and neutrosophic TOPSIS method［J］. Engineering Applications of Artificial Intelligence， 2023， 124： No.106599.
29	SNYDER K， PAULSON P， BERGEN S. A website assessment tool for patient engagement： a verification［J］. International Journal of Healthcare Management， 2020， 13（1）： 58-64.
30	MILLER G A. WordNet： a lexical database for English［J］. Communications of the ACM， 1995， 38（11）： 39-41.
31	MANNING C D， SURDEANU M， BAUER J， et al. The Stanford CoreNLP natural language processing toolkit［C］// Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics： System Demonstrations. Stroudsburg： ACL， 2014： 55-60.
32	Binomial proportion confidence interval［EB/OL］. ［2024-04-30］. .
33	北京大学信息科学技术学院计算语言学研究所，郑州大学信息工程学院自然语言处理实验室，鹏城实验室人工智能研究中心智慧医疗课题组. 中文医学知识图谱［DB/OL］. ［2024-06-05］. .
	The Institute of Computational Linguistics of Peking University， Natural Language Processing Laboratory of School of Information Engineering of Zhengzhou University， Artificial Intelligence Research Center Smart Healthcare Research Group of Pengcheng Laboratory. Chinese medical knowledge graph［DB/OL］. ［2024-06-05］. .
34	HE K， ZHANG X， REN S， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 770-778.
35	BA J L， KIROS J R， HINTON G E. Layer normalization［EB/OL］. ［2024-04-30］..
36	CHEN T， KORNBLITH S， NOROUZI M， et al. A simple framework for contrastive learning of visual representations［C］// Proceedings of the 37th International Conference on Machine Learning. New York： JMLR.org， 2020： 1597-1607.
37	TIAN B， ZHANG Y， CHEN X， et al. DRGAN： a GAN-based framework for doctor recommendation in Chinese on-line QA communities［C］// Proceedings of the 2019 International Workshops on Database Systems for Advanced Applications， LNCS 11448. Cham： Springer， 2019： 444-447.
38	YAN Y， YU G， YAN X. Online doctor recommendation with convolutional neural network and sparse inputs［J］. Computational Intelligence and Neuroscience， 2020， 2020： No.8826557.
39	YUAN H， DENG W. Doctor recommendation on healthcare consultation platforms： an integrated framework of knowledge graph and deep learning［J］. Internet Research， 2022， 32（2）： 454-476.
40	LU X， ZHANG Y， 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 （Volume 1： Long Papers）. Stroudsburg： ACL， 2022： 1111-1123.
41	GENG S， TAO B， LIANG G， et al. Temporal knowledge graph attention network for online doctor recommendation［C］// Proceedings of the 8th International Conference on Intelligent Information Processing. New York： ACM， 2023： 277-282.

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Consultation recommendation method based on knowledge graph and dialogue structure

基于知识图谱和对话结构的问诊推荐方法

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