Collaborative Construction Method of Biomedical Knowledge Graph Based on Multi-Blockchain

Author:

Wang Jinlong1ORCID,Xie Zhenxi1ORCID,Xin Hui1ORCID,Li Pengjun1ORCID,Zhang Yuanyuan1ORCID,Xiong Xiaoyun1ORCID,Lin Jerry Chun-Wei2

Affiliation:

1. Qingdao University of Technology, China

2. Silesian University of Technology, Poland

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.

Publisher

Association for Computing Machinery (ACM)

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