Abstract
The success of open-access knowledge graphs, such as YAGO, and commercial products, such as Google Knowledge Graph, has attracted much attention from both academic and industrial communities in building common-sense and domain-specific knowledge graphs. A natural question arises that how to effectively and efficiently manage a large-scale knowledge graph. Though systems and technologies that use relational storage engines or native graph database management systems are proposed, there exists no widely accepted solution. Therefore, a benchmark for management of knowledge graphs is required.
In this paper, we analyze the requirements of benchmarking knowledge graph management from a specific yet important point-of-view, i.e. characteristics of knowledge graph data. Seventeen statistical features of four knowledge graphs as well as two social networks are studied. We show that through these graphs depict similar structures, their tiny differences may result in totally different storage and indexing strategies, that should not be omitted. Finally, we put forward the requirements to seeding datasets and synthetic data generators for benchmarking knowledge graph management based on the study.
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Acknowledgement
This work is partially supported by National Hightech R&D Program (863 Program) under grant number 2015AA015307, and National Science Foundation of China under grant numbers 61432006 and 61170086. The authors would also like to thank Ping An Technology (Shenzhen) Co., Ltd. for the support of this research.
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Cheng, W., Wang, C., Xiao, B., Qian, W., Zhou, A. (2016). On Statistical Characteristics of Real-Life Knowledge Graphs. In: Zhan, J., Han, R., Zicari, R. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2015. Lecture Notes in Computer Science(), vol 9495. Springer, Cham. https://doi.org/10.1007/978-3-319-29006-5_4
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