Research Progress of Recommendation System Based on Medical Knowledge Graph

doi:10.3778/j.issn.1002-8331.2301-0006

Abstract

Abstract: Medical knowledge graph can provide new auxiliary information for recommendation system because of its structural semantic knowledge characteristics. The combination of recommendation system and medical knowledge graph can not only effectively alleviate the problem of sparse data, but also enhance the accuracy and interpretability of recommendation results, thus realizing personalized recommendation of medical information. Aiming at the characteristics of strong professional barriers and various conceptual terms in the medical field, the medical knowledge graph structure is systematically sorted out. This paper summarizes the traditional recommendation algorithms and compares their advantages and disadvantages. It summarizes and introduces the recommendation systems based on path, embedding and fusion respectively, focusing on the research results of combining medical practice and their advantages and disadvantages. The existing problems and challenges in current research are analyzed, and the feasible future research direction is prospected.

Key words: knowledge graph, intelligent medicine, recommendation system, natural language processing

摘要： 医疗知识图谱因其结构化的语义知识特点，可以为推荐系统提供新型的辅助信息。推荐系统与医疗知识图谱相结合，不仅能有效缓解数据稀疏等问题，还增强了推荐结果的准确性以及可解释性，从而实现医疗信息的个性化推荐。针对医疗领域专业壁垒坚固、概念术语繁多等特点，对医疗知识图谱架构进行了系统梳理；总结传统推荐算法，并对比分析了其优缺点；对基于路径、基于嵌入和基于融合的推荐系统分别总结介绍，重点对结合医疗实践的研究成果及其优缺点进行归纳总结；对具可行性的未来研究方向进行了展望。

关键词: 知识图谱, 智能医疗, 推荐系统, 自然语言处理

SHEN Xiyu, CAI Xiaohong, CAO Hui. Research Progress of Recommendation System Based on Medical Knowledge Graph[J]. Computer Engineering and Applications, 2023, 59(19): 40-51.

沈希宇, 蔡肖红, 曹慧. 融合医疗知识图谱的推荐系统研究进展[J]. 计算机工程与应用, 2023, 59(19): 40-51.

References

[1] HALDER K，KAN M Y，SUGIYAMA K.Health forum thread recommendation using an interest aware topic model[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management，2017：1589-1598.
[2] SUN F，YU M，ZHANG X，et al.A vocabulary recommendation system based on knowledge graph for chinese language learning[C]//Proceedings of the 2020 IEEE 20th International Conference on Advanced Learning Technologies（ICALT），2020：210-212.
[3] LINDEN G，SMITH B，YORK J.Amazon.com recommendations：item-to-item collaborative filtering[J].IEEE Internet Computing，2003，7（1）：76-80.
[4] RICHARDSON M，DOMINOWSKA E，RAGNO R.Predicting clicks：estimating the click-through rate for new ads[C]//Proceedings of the 16th International Conference on World Wide Web，2007：521-530.
[5] RENDLE S.Factorization machines[C]//Proceedings of the 2010 IEEE International Conference on Data Mining，2010：995-1000.
[6] KE G，MENG Q，FINLEY T，et al.Lightgbm：a highly efficient gradient boosting decision tree[C]//Advances in Neural Information Processing Systems，2017.
[7] HE X，PAN J，JIN O，et al.Practical lessons from predicting clicks on ads at facebook[C]//Proceedings of the 8th International Workshop on Data Mining for Online Advertising，2014：1-9.
[8] KRIZHEVSKY A，SUTSKEVER I，HINTON G E J C O T A.Imagenet classification with deep convolutional neural networks[J].Communications of the ACM，2017，60（6）：84-90.
[9] KOREN Y，BELL R，VOLINSKY C J C.Matrix factorization techniques for recommender systems[J].Computer，2009，42（8）：30-37.
[10] JUAN Y，ZHUANG Y，CHIN W S，et al.Field-aware factorization machines for CTR prediction[C]//Proceedings of the 10th ACM Conference on Recommender Systems，2016：43-50.
[11] YU X，REN X，GU Q，et al.Collaborative filtering with entity similarity regularization in heterogeneous information networks[C]//Proceedings of the IJCAI-13 HINA Workshop，2013.
[12] YU X，REN X，SUN Y，et al.Personalized entity recommendation：a heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining，2014：283-292.
[13] SHI C，ZHANG Z，LUO P，et al.Semantic path based personalized recommendation on weighted heterogeneous information networks[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management，2015：453-462.
[14] ZHAO H，YAO Q，LI J，et al.Meta-graph based recommendation fusion over heterogeneous information networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2017：635-644.
[15] HU B，SHI C，ZHAO W X，et al.Leveraging meta-path based context for top-n recommendation with a neural co-attention model[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2018：1531-1540.
[16] WANG X，WANG D，XU C，et al.Explainable reasoning over knowledge graphs for recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：5329-5336.
[17] XIAN Y，FU Z，ZHAO H，et al.CAFE：coarse-to-fine neural symbolic reasoning for explainable recommendation[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management，2020：1645-1654.
[18] WANG X，HUANG T，WANG D，et al.Learning intents behind interactions with knowledge graph for recommendation[C]//Proceedings of the Web Conference 2021，2021：878-887.
[19] ZHANG C，WANG Y，ZHU L，et al.Multi-graph heterogeneous interaction fusion for social recommendation[J].ACM Transactions on Information Systems（TOIS），2021，40（2）：1-26.
[20] ZHANG F，YUAN N J，LIAN D，et al.Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2016：353-362.
[21] 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，2018：1835-1844.
[22] YANG D，GUO Z，WANG Z，et al.A knowledge-enhanced deep recommendation framework incorporating gan-based models[C]//Proceedings of the 2018 IEEE International Conference on Data Mining（ICDM），2018：1368-1373.
[23] WANG H，ZHANG F，ZHAO M，et al.Multi-task feature learning for knowledge graph enhanced recommendation[C]//Proceedings of the World Wide Web Conference，2019：2000-2010.
[24] CAO Y，WANG X，HE X，et al.Unifying knowledge graph learning and recommendation：towards a better understanding of user preferences[C]//Proceedings of the World Wide Web Conference，2019：151-161.
[25] YE Y，WANG X，YAO J，et al.Bayes embedding（BEM）refining representation by integrating knowledge graphs and behavior-specific networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management，2019：679-688.
[26] LEE D，OH B，SEO S，et al.News recommendation with topic-enriched knowledge graphs[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management，2020：695-704.
[27] ZEB A，HAQ A U，ZHANG D，et al.KGEL：a novel end-to-end embedding learning framework for knowledge graph completion[J].Expert Systems with Applications，2021，167：114164.
[28] CUI Y，SUN H，ZHAO Y，et al.Sequential-knowledge-aware next POI recommendation：a meta-learning approach[J].ACM Transactions on Information Systems（TOIS），2021，40（2）：1-22.
[29] WANG H，ZHANG F，WANG J，et al.RippleNet：propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on information and Knowledge Management，2018：417-426.
[30] QU Y，BAI T，ZHANG W，et al.An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation[C]//Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data，2019：1-9.
[31] ZHAO J，ZHOU Z，GUAN Z，et al.IntentGC：a scalable graph convolution framework fusing heterogeneous information for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2019：2347-2357.
[32] WANG X，HE X，CAO Y，et al.KGAT：knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2019：950-958.
[33] WANG H，ZHAO M，XIE X，et al.Knowledge graph convolutional networks for recommender systems[C]//Proceedings of the World Wide Web Conference，2019：3307-3313.
[34] SUN R，CAO X，ZHAO Y，et al.Multi-modal knowledge graphs for recommender systems[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management，2020：1405-1414.
[35] TU K，CUI P，WANG D，et al.Conditional graph attention networks for distilling and refining knowledge graphs in recommendation[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management，2021：1834-1843.
[36] XU Z，LIU H，LI J，et al.CKGAT：collaborative knowledge-aware graph attention network for top-N recommendation[J].Applied Sciences，2022，12（3）：1669.
[37] YU X，REN X，SUN Y，et al.Recommendation in heterogeneous information networks with implicit user feedback[C]//Proceedings of the 7th ACM Conference on Recommender Systems，2013：347-350.
[38] LUO C，PANG W，WANG Z，et al.Hete-CF：social-based collaborative filtering recommendation using heterogeneous relations[C]//Proceedings of the 2014 IEEE International Conference on Data Mining，2014：917-922.
[39] WANG N，CAI X，YANG L，et al.Safe medicine recommendation via star interactive enhanced-based transformer model[J].Computers in Biology and Medicine，2022，141：105159.
[40] NAM Y，KIM M，CHANG H S，et al.Drug repurposing with network reinforcement[J].BMC Bioinformatics，2019，20（13）：1-10.
[41] ZHAO C，JIANG J，GUAN Y，et al.EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning[J].Artificial Intelligence in Medicine，2018，87：49-59.
[42] LAN Y，HE S，LIU K，et al.Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion[J].BMC Medical Informatics and Decision Making，2021，21（9）：1-12.
[43] BORDES A，USUNIER N，GARCIA-DURAN A，et al.Translating embeddings for modeling multi-relational data[C]//Advances in Neural Information Processing Systems，2013.
[44] LIN Y，LIU Z，SUN M，et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence，2015.
[45] NICKEL M，TRESP V，KRIEGEL H P.A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th International Conference on International Conference on Machine Learning，2011：809-816.
[46] YANG B，YIH W T，HE X，et al.Embedding entities and relations for learning and inference in knowledge bases[J].arXiv：1412.6575，2014.
[47] ZHANG Y，WALLACE B J A P A.A sensitivity analysis of（and practitioners’ guide to） convolutional neural networks for sentence classification[J].arXiv：1510.03820，2015.
[48] YE Q，HSIEH C Y，YANG Z，et al.A unified drugtarget interaction prediction framework based on knowledge graph and recommendation system[J].Nature Communications，2021，12（1）：1-12.
[49] SHANG J，XIAO C，MA T，et al.GAMENet：graph augmented memory networks for recommending medication combination[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：1126-1133.
[50] ZENG X，SONG X，MA T，et al.Repurpose open data to discover therapeutics for COVID-19 using deep learning[J].Journal of Proteome Research，2020，19（11）：4624-4636.
[51] JIN Y，JI W，ZHANG W，et al.A KG-enhanced multi-graph neural network for attentive herb recommendation[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics，2021，19（5）：2560-2571.
[52] GOGLEVA A，POLYCHRONOPOULOS D，PFEIFER M，et al.Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer[J].Nature Communications，2022，13（1）：1-14.
[53] GONG F，WANG M，WANG H，et al.SMR：medical knowledge graph embedding for safe medicine recommendation[J].Big Data Research，2021，23：100174.
[54] SOUSA D，COUTO F M J I J O B，INFORMATICS H.Biomedical relation extraction with knowledge graph-based recommendations[J].IEEE Journal of Biomedical and Health Informatics，2022，26（8）：4207-4217.
[55] SOUSA D，COUTO F M.BiOnt：deep learning using multiple biomedical ontologies for relation extraction[C]//Proceedings of the European Conference on Information Retrieval，2020：367-374.
[56] WANG H，ZHANG F，ZHANG M，et al.Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2019：968-977.
[57] WANG Y，DONG L，LI Y，et al.Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet[J].Plos One，2021，16（5）：e0251162.
[58] MAO C，YAO L，LUO Y.MedGCN：medication recommendation and lab test imputation via graph convolutional networks[J].Journal of Biomedical Informatics，2022，127：104000.
[59] YANG Y，RAO Y，YU M，et al.Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation[J].Neural Networks，2022，146：1-10.
[60] LIU W，YIN L，WANG C，et al.Multitask healthcare management recommendation system leveraging knowledge graph[J].Journal of Healthcare Engineering，2021（1）：1233483.
[61] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.