Abstract
The development of knowledge graph needs the support of a vast quantity of data. However, the amount of data increases rapidly is placing increasing demands on machines. Centralized data storage requires high-performance hosts to store data, which is costly and have single point of failure. Distributed data storage can reduce the cost of the machine greatly, and there is no single point of failure, but it has requirements for partition and storage of data collection. In the knowledge storage of specific domain, the way of graph data partition and storage vary from the different domain knowledge. To solve the above problems, a scheme of graph partition and distributed storage for domain-specific knowledge graphs is proposed. The proposed graph partition scheme pays attention to the correlation between the data, and divides the nodes affiliated each other into the same or similar partition. A distributed aggregation storage scheme is designed, which makes full use of cluster performance and solves the problem of data consistency during data insertion and update. The proposed distributed storage scheme based on HBase combines Neo4j to realize visual query effectively. Experimental results show the efficiency and the effectiveness of the proposed method in partition time, the number of edge-cut and update time.
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Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant (No. 61472169, 61502215, 62072220, 61702381, U1811261); China Postdoctoral Science Foundation Funded Project (2020M672134); Science Research Fund of Liaoning Province Education Department (LJC201913); Liaoning Public Opinion and Network Security Big Data System Engineering Laboratory (No. 04-2016-0089013).
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Shan, X., Shi, X., Ma, W., Wang, J. (2021). Distributed Storage and Query for Domain Knowledge Graphs. In: Chen, Q., Li, J. (eds) Web and Big Data. APWeb-WAIM 2020 International Workshops. APWeb-WAIM 2020. Communications in Computer and Information Science, vol 1373. Springer, Singapore. https://doi.org/10.1007/978-981-16-0479-9_10
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DOI: https://doi.org/10.1007/978-981-16-0479-9_10
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