基于关系图谱的科技数据分析算法

doi:10.11896/jsjkx.191200154

计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 174-179.doi: 10.11896/jsjkx.191200154

• 数据库&大数据&数据科学 • 上一篇下一篇

基于关系图谱的科技数据分析算法

张寒烁, 杨冬菊

北方工业大学大规模流数据集成与分析技术北京市重点实验室北京100144
北方工业大学云计算研究中心北京100144

收稿日期:2019-12-25 修回日期:2020-05-28 出版日期:2021-03-15 发布日期:2021-03-05
通讯作者: 杨冬菊(yangdongju@ncut.edu.cn)
作者简介:hanshuo_1994@foxmail.com
基金资助:
国家重点研发计划课题(2019YFB1405103)

Technology Data Analysis Algorithm Based on Relational Graph

ZHANG Han-shuo, YANG Dong-ju

Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data,North China University of Technology,Beijing 100144,China
Research Center for Cloud Computing,North China University of Technology,Beijing 100144,China

Received:2019-12-25 Revised:2020-05-28 Online:2021-03-15 Published:2021-03-05
About author:ZHANG Han-shuo,born in 1994,postgraduate.His main research interests include service computing,cloud computing and big data.
YANG Dong-ju,born in 1975,Ph.D,associate professor,is a member of China Computer Federation.Her main research interests include service computing,data integration,cloud computing,cloud storage,and their applications in industry data center.
Supported by:
National Key Research and Development Project of China(2019YFB1405103).

摘要/Abstract

摘要： 随着科技数据量的不断增长,各科技部门积累了大量科技项目的科技管理数据。对于大量结构化数据,需要针对分散数据进行整理、分析,最终按需求提供数据查询与抽取服务。由于在关系数据库中关联关系的分析效果不佳,为了提高分析效率,文中引入了关系图谱进行数据处理。首先,提出了一种基于词频的实体搜索与定位算法来提取实体和关系,并构建关系图谱;其次,对关系图谱进行分析,提出了一种基于改进FP-growth的图数据频繁项挖掘算法;然后,设计了基于图数据的数据筛选流程,进行数据的筛选、分析,并定义评分矩阵,对待筛选数据情况进行评价,最终给出分析意见,且数据筛选的评价标准可以进行自定义;最后,结合构建的关系图谱,将算法进行了实际应用,并将其封装为服务。实验结果表明,提出的基于改进FP-growth的频繁项挖掘算法相比传统FP-growth算法在时间上有10%～12%的提升,数据筛选过程的准确率达到97%左右。

关键词: 服务应用, 关系图谱, 人员关系图谱, 数据分析, 数据挖掘, 图谱构建

Abstract: With the continuous growth of scientific and technological data,various science and technology departments have accumulated a large number of scientific and technological management data of scientific and technological projects.For a large amount of structured data,it is necessary to organize and analyze the distributed data,and finally provide data query and extraction ser-vices according to requirements.The analysis of relationships in relational databases is not effective.In order to improve the efficiency of analysis,relational graphs are introduced for data processing.Firstly,an entity search and localization algorithm based on word frequency is proposed,and the entities and relationships are extracted to construct the relationalgraph.Secondly,an improved FP-growth algorithm for frequent item mining of graph data is proposed in order to solve the frequent item screening problem in the graph data.Then,a data filtering process based on graph data is designed.In addition,this paper defines the scoring matrix,evaluate the screening data,and finally give an analysis opinion.The evaluation standard of data screening can be customized.Finally,combined with the constructed relational graph,the algorithm is applied in practice and encapsulated as a ser-vice.Experimentalresults show that the improved FP-growth-based frequent item mining algorithm has 10%~12% improvement over the traditional FP-growth algorithm.The accuracy of the data screening process designed in this paper reaches 97%.

Key words: Construction of human relation graph, Data analysis, Data mining, Graph construction, Relational graph, Service application

中图分类号:

TP391

张寒烁, 杨冬菊. 基于关系图谱的科技数据分析算法[J]. 计算机科学, 2021, 48(3): 174-179. https://doi.org/10.11896/jsjkx.191200154

ZHANG Han-shuo, YANG Dong-ju. Technology Data Analysis Algorithm Based on Relational Graph[J]. Computer Science, 2021, 48(3): 174-179. https://doi.org/10.11896/jsjkx.191200154

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基于关系图谱的科技数据分析算法

Technology Data Analysis Algorithm Based on Relational Graph

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