Construction of Korean Medicine Knowledge Graph and Development of Prescription Recommendation System based on RippleNet

Article information

J Korean Med. 2025;46(2):51-62

Publication date (electronic) : 2025 June 1

doi : https://doi.org/10.13048/jkm.25017

Sang-Kyun Kim ¹

, DongHyoung Lee ²

, Anna Kim ¹

, SeJin Nam ²

¹KM Data Division, Korea Institute of Oriental Medicine

²Data Convergence Team, Seoul National University Bundang Hospital

Correspondence to: Sang-Kyun Kim, 1672, Yuseong-daero, Yuseong-gu, Daejeon, South Korea, Tel: +82-42-868-9526, E-mail: skkim@kiom.re.kr

Received 2025 March 26; Revised 2025 April 16; Accepted 2025 May 22.

Abstract

Objectives

We aim to propose an artificial intelligence system for recommending Korean medicine prescriptions based on the RippleNet algorithm using a knowledge graph.

Methods

The Korean medicine knowledge graph was constructed with information on prescriptions, medicinal materials, chemical compounds, gene targets, and modern diseases from the TM-MC database. The RippleNet algorithm was trained with the knowledge graph and the prescription history of diseases from TM-MC. The optimized hyperparameter values of the algorithm were derived using the grid search method. The recommendation system was implemented with the knowledge graph and the parameterized model.

Results

The Korean medicine knowledge graph contains 16,977 nodes and 142,924 edges, and the prescription histories of diseases include 2,101 true values and 3,413 false data points. The recommendation algorithm showed the best performance with an AUC of 0.919 when trained with parameters of 4 hops and a ripple set size of 32.

Conclusions

In this study, we implemented a system that recommends prescriptions based on artificial intelligence algorithms using the knowledge graph. In the future, we plan to improve the quality of the knowledge graph and explore a variety of modern AI algorithms to enhance recommendations.

Keywords: Recommendation system; Prescription; Knowledge graph; RippleNet; Korean medicine

Fig. 1

Progress of the preference propagation in knowledge graph

Fig. 2

Example of Mawhang-tang in Korean medicine knowledge graph

Fig. 3

AUC variation graph of RippleNet algorithm parameters

Fig. 4

Screenshot of the prescription recommendation system

Table 1

Types and the Number of Nodes in Knowledge Graph

Table 2

AUC and ACC for Different Hop Numbers

Table 3

AUC and ACC for Different Sizes of Ripple Set

Table 4

AUC and ACC for Different Embedding Dimensions

Table 5

AUC and ACC for Different Batch Sizes

Table 6

AUC and ACC for Different L2 Regularization Weights

Table 7

AUC and ACC for Different KGE Weights

Table 8

AUC and ACC for Different Learning Rates

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