Just Accepted
Collaborative Construction Method of Biomedical Knowledge Graph Based on Multi-Blockchain
Authors: 
Jinlong Wang, Zhenxi Xie, Hui Xin, Pengjun Li, Yuanyuan Zhang, Xiaoyun Xiong, Jerry Chun-Wei LinAuthors Info & Claims
Jinlong Wang, Zhenxi Xie, Hui Xin, Pengjun Li, Yuanyuan Zhang, Xiaoyun Xiong, Jerry Chun-Wei LinAuthors Info & ClaimsAccepted on 01 June 2024
Abstract
The collaborative construction method of knowledge graph based on blockchain is crucial for the safe and efficient construction of Biomedical Knowledge Graph (BioKG), and has received widespread attention from the academic and medical circles. At present, the data sources of Biomedical Knowledge Graph are very wide. While using multi-source data to collaboratively build a knowledge graph, data redundancy problems often occur. Once data from a large number of sources is available, it becomes difficult for the system to ensure data consistency in a reliable way. In addition, the increase in inconsistent and redundant data also reduces the efficiency of collaboration in the system. The traditional blockchain-based collaborative construction method of BioKG is difficult to effectively solve these problems. Multi-Blockchain technology provides a new solution for the construction of collaborative knowledge graphs in the biomedical field, which is characterized by good scalability and diversified applications. To this end, we propose a multi-person collaboration construction method for BioKG based on multi-blockchain, named collaborative construction method Chain (CCMC). CCMC uses the multi-chain structure and clock cycle control method of the blockchain to separate user operations and system verification, and uses the read-write separation function to improve the consistency of the collaborative version. In addition, we propose a semantic fusion method that effectively reduces data redundancy in the collaborative process and improves the efficiency of BioKG collaborative construction. Performance testing and comparative evaluation confirmed that compared with previous methods, CCMC significantly improves the creation of collaborative knowledge graphs and meets the requirements of multi-person collaborative construction.
References
[1]
Alexander Bernier, Fruzsina Molnár-Gábor, Bartha M Knoppers, Pascal Borry, Priscilla MDG Cesar, Thijs Devriendt, Melanie Goisauf, Madeleine Murtagh, Pilar Nicolás Jiménez, Mikel Recuero, et al. 2023. Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory. European Journal of Human Genetics (2023), 1–8.
[2]
Yasin Celik, Ioan Petri, and Masoud Barati. 2023. Blockchain supported BIM data provenance for construction projects. Computers in Industry 144 (2023), 103768.
[3]
Yasin Celik, Ioan Petri, and Yacine Rezgui. 2023. Integrating BIM and Blockchain across construction lifecycle and supply chains. Computers in Industry 148 (2023), 103886.
[4]
Huajun Chen, Ning Hu, Guilin Qi, Haofen Wang, Zhen Bi, Jie Li, and Fan Yang. 2021. Openkg chain: A blockchain infrastructure for open knowledge graphs. Data Intelligence 3, 2 (2021), 205–227.
[5]
Ali Dorri, Marco Steger, Salil S Kanhere, and Raja Jurdak. 2017. Blockchain: A distributed solution to automotive security and privacy. IEEE Communications Magazine 55, 12 (2017), 119–125.
[6]
Keke Gai, Jinnan Guo, Liehuang Zhu, and Shui Yu. 2020. Blockchain meets cloud computing: A survey. IEEE Communications Surveys & Tutorials 22, 3 (2020), 2009–2030.
[7]
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al. 2012. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research 40, D1 (2012), D1100–D1107.
[8]
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering 34, 8 (2020), 3549–3568.
[9]
Nikolai Hecker, Jessica Ahmed, Joachim Von Eichborn, Mathias Dunkel, Karel Macha, Andreas Eckert, Michael K Gilson, Philip E Bourne, and Robert Preissner. 2012. SuperTarget goes quantitative: update on drug–target interactions. Nucleic acids research 40, D1 (2012), D1113–D1117.
[10]
Zakwan Jaroucheh, Mohamad Alissa, William J Buchanan, and Xiaodong Liu. 2020. TRUSTD: Combat fake content using blockchain and collective signature technologies. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 1235–1240.
[11]
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. 2021. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE transactions on neural networks and learning systems 33, 2 (2021), 494–514.
[12]
Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh V Chawla, and Meng Jiang. 2020. Biomedical knowledge graphs construction from conditional statements. IEEE/ACM transactions on computational biology and bioinformatics 18, 3 (2020), 823–835.
[13]
Sun Jianjun, Li Ming, and Ma Jingang. 2020. Research and application of data sharing platform integrating Ethereum and IPFs Technology. In 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 279–282.
[14]
Marek Kim, Henrym Laskowski. 2018. Toward an ontology-driven blockchain design for supply-chain provenance. Intelligent Systems in Accounting, Finance and Management 25, 1 (2018), 18–27.
[15]
Sunghwan Kim, Paul A Thiessen, Evan E Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, Benjamin A Shoemaker, et al. 2016. PubChem substance and compound databases. Nucleic acids research 44, D1 (2016), D1202–D1213.
[16]
Timotej Knez, Domen Gašperlin, Marko Bajec, and Slavko Žitnik. 2022. Blockchain-based transaction manager for ontology databases. Informatica 33, 2 (2022), 343–364.
[17]
Stefanos Leonardos, Daniël Reijsbergen, and Georgios Piliouras. 2020. Weighted voting on the blockchain: Improving consensus in proof of stake protocols. International Journal of Network Management 30, 5 (2020), e2093.
[18]
Chaoyang Li, Mianxiong Dong, Jian Li, Gang Xu, Xiu-Bo Chen, Wen Liu, and Kaoru Ota. 2022. Efficient medical big data management with keyword-searchable encryption in healthchain. IEEE Systems Journal 16, 4 (2022), 5521–5532.
[19]
Jiatao Li, Dezhi Han, Zhongdai Wu, Junxiang Wang, Kuan-Ching Li, and Arcangelo Castiglione. 2023. A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control. Future Generation Computer Systems 142 (2023), 195–211.
[20]
Xi Lin, Jianhua Li, Jun Wu, Haoran Liang, and Wu Yang. 2019. Making knowledge tradable in edge-AI enabled IoT: A consortium blockchain-based efficient and incentive approach. IEEE Transactions on Industrial Informatics 15, 12 (2019), 6367–6378.
[21]
Yinbin Miao, Ziteng Liu, Hongwei Li, Kim-Kwang Raymond Choo, and Robert H Deng. 2022. Privacy-preserving Byzantine-robust federated learning via blockchain systems. IEEE Transactions on Information Forensics and Security 17 (2022), 2848–2861.
[22]
Sameh K Mohamed, Aayah Nounu, and Vít Nováček. 2021. Biological applications of knowledge graph embedding models. Briefings in bioinformatics 22, 2 (2021), 1679–1693.
[23]
Jonathan M Mortensen, Matthew Horridge, Mark A Musen, and Natalya F Noy. 2012. Applications of ontology design patterns in biomedical ontologies. In AMIA annual symposium proceedings, Vol. 2012. American Medical Informatics Association, 643.
[24]
Omaji Samuel, Akogwu Blessing Omojo, Abdulkarim Musa Onuja, Yunisa Sunday, Prayag Tiwari, Deepak Gupta, Ghulam Hafeez, Adamu Sani Yahaya, Oluwaseun Jumoke Fatoba, and Shahab Shamshirband. 2022. IoMT: A COVID-19 healthcare system driven by federated learning and blockchain. IEEE Journal of Biomedical and Health Informatics 27, 2 (2022), 823–834.
[25]
Chandana Tennakoon, Nazar Zaki, Hiba Arnaout, Shady Elbassuoni, Wassim El-Hajj, and Alanoud Al Jaberi. 2019. Biological knowledge graph construction, search, and navigation. In Leveraging Biomedical and Healthcare Data. Elsevier, 107–120.
[26]
Allan Third and John Domingue. 2017. Linked data indexing of distributed ledgers. In Proceedings of the 26th International Conference on World Wide Web Companion. 1431–1436.
[27]
Jinlong Wang, Yumin Shen, Xiaoyun Xiong, Xu Wang, and Xiaoxue Fang. 2022. Research on multi-person collaborative design of BIM drawing based on blockchain. Scientific Reports 12, 1 (2022), 16312.
[28]
Shuai Wang, Chenchen Huang, Juanjuan Li, Yong Yuan, and Fei-Yue Wang. 2019. Decentralized construction of knowledge graphs for deep recommender systems based on blockchain-powered smart contracts. IEEE Access 7 (2019), 136951–136961.
[29]
David S Wishart, Craig Knox, An Chi Guo, Dean Cheng, Savita Shrivastava, Dan Tzur, Bijaya Gautam, and Murtaza Hassanali. 2008. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic acids research 36, suppl_1 (2008), D901–D906.
[30]
Tianxiu Xie, Keke Gai, Liehuang Zhu, Yunwei Guo, and Kim-Kwang Raymond Choo. 2023. Cross-Chain-Based Trustworthy Node Identity Governance in Internet of Things. IEEE Internet of Things Journal (2023).
[31]
Chenhan Xu, Kun Wang, Peng Li, Song Guo, Jiangtao Luo, Baoliu Ye, and Minyi Guo. 2018. Making big data open in edges: A resource-efficient blockchain-based approach. IEEE Transactions on Parallel and Distributed Systems 30, 4 (2018), 870–882.
[32]
Fan Xue and Weisheng Lu. 2020. A semantic differential transaction approach to minimizing information redundancy for BIM and blockchain integration. Automation in construction 118 (2020), 103270.
[33]
Yading Yuan, Ming Chao, and Yeh-Chi Lo. 2017. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE transactions on medical imaging 36, 9 (2017), 1876–1886.
[34]
Xiangxiang Zeng, Xinqi Tu, Yuansheng Liu, Xiangzheng Fu, and Yansen Su. 2022. Toward better drug discovery with knowledge graph. Current opinion in structural biology 72 (2022), 114–126.
[35]
Yue Zhang, Keke Gai, Jiang Xiao, Liehuang Zhu, and Kim-Kwang Raymond Choo. 2022. Blockchain-empowered efficient data sharing in Internet of Things settings. IEEE Journal on Selected Areas in Communications 40, 12 (2022), 3422–3436.
[36]
Yuanyi Zhen, Lanqin Zheng, and Penghe Chen. 2021. Constructing knowledge graphs for online collaborative programming. IEEE Access 9 (2021), 117969–117980.
[37]
Chenghu Zhou, Hua Wang, Chengshan Wang, Zengqian Hou, Zhiming Zheng, Shuzhong Shen, Qiuming Cheng, Zhiqiang Feng, Xinbing Wang, Hairong Lv, et al. 2021. Geoscience knowledge graph in the big data era. Science China Earth Sciences 64, 7 (2021), 1105–1114.
[38]
Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-Rong Wen, and Jingsong Yu. 2020. Improving conversational recommender systems via knowledge graph based semantic fusion. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 1006–1014.
[39]
Xiangrong Zhu, Honghu Zhu, and Wei Hu. 2022. FactChain: A blockchain-based crowdsourcing knowledge fusion system. Softwore 33, 10 (10 2022), 3546.
Index Terms
- Collaborative Construction Method of Biomedical Knowledge Graph Based on Multi-Blockchain
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Association for Computing Machinery
New York, NY, United States
Publication History
Online AM: 29 June 2024
Accepted: 01 June 2024
Revised: 28 November 2023
Received: 18 April 2023
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