面向图数据管理系统基准评测的知识图谱统计特征分析

钱卫宁; 孙晨; 程文亮; 周傲英

doi:10.11959/j.issn.2096-0271.2016049

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面向图数据管理系统基准评测的知识图谱统计特征分析

研究 | 更新时间：2024-06-03

- 面向图数据管理系统基准评测的知识图谱统计特征分析
- Statistical characteristics analysis of knowledge graphs for benchmarking graph database management systems
- 大数据 2016年2卷第5期页码：2016049
- 作者机构：
- 作者简介：
  
  [ "钱卫宁（1976-），男，华东师范大学数据科学与工程研究院教授、博士生导师，主要研究方向为互联网环境下的数据管理、大数据管理系统评测基准、社交媒体数据分析、知识图谱构建与应用等。" ]
  [ "孙晨（1995-），女，华东师范大学数据科学与工程研究院硕士生，主要研究方向为知识图谱构建。" ]
  [ "程文亮（1989-），男，华东师范大学数据科学与工程研究院硕士生，主要研究方向为数据挖掘与知识发现。" ]
  [ "周傲英（1965-），男，华东师范大学副校长，长江学者特聘教授，数据科学与工程研究院院长，主要研究方向为Web数据管理、数据密集型计算、内存集群计算、分布事务处理、大数据基准测试和性能优化。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61432006)
- DOI：10.11959/j.issn.2096-0271.2016049
  中图分类号： TP31
- 网络首发：2016-09，
  
  纸质出版：2016-09-20
- 稿件说明：
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钱卫宁, 孙晨, 程文亮, 等. 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):2016049.

Weining QIAN, Chen SUN, Wenliang CHENG, et al. Statistical characteristics analysis of knowledge graphs for benchmarking graph database management systems[J]. Big data research, 2016, 2(5): 2016049.
钱卫宁, 孙晨, 程文亮, 等. 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):2016049. DOI： 10.11959/j.issn.2096-0271.2016049.

Weining QIAN, Chen SUN, Wenliang CHENG, et al. Statistical characteristics analysis of knowledge graphs for benchmarking graph database management systems[J]. Big data research, 2016, 2(5): 2016049. DOI： 10.11959/j.issn.2096-0271.2016049.

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。

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.

关键词

Keywords

references

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