面向图数据管理系统基准评测的知识图谱统计特征分析

doi:10.11959/j.issn.2096-0271.2016049

摘要/Abstract

摘要：

近年来，图结构数据在信息安全、科学研究、互联网服务等各个领域被广泛采用，图数据管理系统也随之快速发展。然而，当前最主要的图数据管理系统评测基准都是面向社交网络服务和分析应用而设计和开发的。通过对知识图谱（knowledge graph）这一类快速发展的图结构数据的统计特征进行分析，并和社交网络进行比较，展示知识图谱和社交网络的显著区别，以此说明现有图数据管理系统基准评测无法满足知识图谱管理的需要，进一步展望图数据管理系统基准评测的需求和发展。

关键词: 基准评测, 图数据, 知识图谱, 统计特征

Abstract:

Recently,graph data has been widely used in domains such as information security,scientific research,internet services,etc.,that stimulates the fast development of graph data management systems.However,existing benchmarks for graph databases are all designed for applications that manage and analyze social networks.The statistical characteristics of knowledge graphs were analyzed,and compared with two social networks.It was showed that knowledge graphs,as an important and fast growing kind of graph data,were significantly different from social networks.Therefore,existing social network based benchmarks were not suitable for applications that deal with knowledge graphs.Furthermore,the requirements for a new benchmark were analyzed.

Key words: benchmark, graph data, knowledge graph, statistical measurement

钱卫宁, 孙晨, 程文亮, 周傲英. 面向图数据管理系统基准评测的知识图谱统计特征分析[J]. 大数据, 2016, 2(5): 3-11.

Weining QIAN, Chen SUN, Wenliang CHENG, Aoying ZHOU. Statistical characteristics analysis of knowledge graphs for benchmarking graph database management systems[J]. Big Data Research, 2016, 2(5): 3-11.

图/表 8

图1

图2

图3

图4

图5

图6

图7

图8

参考文献 17

[1]	DALTON J , DIETZ L , ALLAN J . Entity query feature expansion using knowledge base links[C]// International ACM Sigir Conference on Research and Development in Information Retrieval,July 6-11,2014,Gold Coast,QLD,Australia. New York: ACM Press, 2014：365-374.
[2]	JOSHI M , SAWANT U , CHAKRABARTI S . Knowledge graph and corpus driven segmentation and answer inference for telegraphic entity-seeking queries[C]// Conference on Empirical Methods in Natural Language Processing,October 25-29,2014,Doha,Qatar.[S.l.:s.n], 2014:1104-1114.
[3]	RAJARAMAN A , ULLMAN J D . Mining of massive datasets[M]. New York: Cambridge University PressPress, 2011.
[4]	ARMSTRONG T G , PONNEKANTI V , BORTHAKUR D ,et al. Linkbench:A database benchmark based on the facebook social graph[C]// ACM SIGMOD International Conference on Management of Data,June 22-27,2013,New York,USA. New York: ACM Press, 2013:1185-1196.
[5]	ERLING O , AVERBUCH A , LARRIBAPEY J ,et al. The ldbc social network benchmark:Interactive workload[C]// ACM SIGMOD International Conference on Management of Data,May 31-June 4,2015,Melbourne,Victoria,Australia. New York: ACM Press, 2015:619-630.
[6]	MA H , WEI J , QIAN W ,et al. On benchmarking online social media analytical queries[C]// International Workshop on Graph Data Management Experiences and Systems,June 23,2013,New York,USA.[S.l.:s.n], 2013:1-7.
[7]	FELLBAUM C . WordNet:an electronic lexical database[M]. Massachusetts:MIT Press, 1998.
[8]	HOFFART J , SUCHANEK F M , BERBERICH K ,et al. Yago2:a spatially and temporally enhanced knowledge base from Wikipedia[J]. Artificial Intelligence, 2013(194): 28-61.
[9]	LEHMANN J , ISELE R , JAKOB M ,et al. Dbpedia:a large-scale,multilingual knowledge base extracted from Wikipedia[J]. Semantic Web Journal, 2015,6(2): 167-195.
[10]	WATTS D , STROGATZ S . Collective dynamics of ‘small-world’ networks[J]. Nature, 1998,393(6684): 440-442.
[11]	LESKOVEC J , SOSIC R . SNAP:a general purpose network analysis and graph mining library[J]. ACM Transactions on Intelligent Systems and Technology, 2016,8(1): 1-20.
[12]	BRODER A , KUMAR R , MAGHOUL F ,et al. Graph structure in the web[J]. Computer Networks, 2000,33(1-6): 309-320.
[13]	KUMAR R , NOVAK J , TOMKINS A . Structure and evolution of online social networks[M]. New York:Springer, 2006:337-357.
[14]	BOCCALETTI S , LATORA V , MORENO Y ,et al. Complex networks:structure and dynamics[J]. Phys Rep, 2006,424(4-5): 175-308.
[15]	CHENG W , WANG C , XIAO B ,et al. On statistical characteristics of reallife knowledge graphs[C]// The Workshop on Big Data Benchmarks,Performance,Optimization,and Emerging Hardware,September 4， 2015,Hawaii,USA.[S.l.:s.n], , 2015:261-267.
[16]	LANCICHINETTI A , FORTUNATO S , RADICCHI F . Benchmark graphs for testing community detection algorithms[J]. Physical Review E Statistical Nonlinear and Soft Matter Physics, 2008,78(2): 561-570.
[17]	钱卫宁, 夏帆, 周敏奇 ,等. 大数据管理系统评测基准的挑战与研究进展[J]. 大数据, 2015,1(1): 82-96. QIAN W N , XIA F , ZHOU M Q ,et al. Challenges and progress of big data management system benchmarks[J]. Big Data Research, 2015,1(1): 82-96.