基于关系图谱的人岗关系研究

doi:10.11959/j.issn.2096-0271.2020059

大数据 ›› 2020, Vol. 6 ›› Issue (6): 0-.doi: 10.11959/j.issn.2096-0271.2020059

• 应用 • 下一篇

基于关系图谱的人岗关系研究

王晓萍¹,郭梦洁^2,³,岳婧雯^2,³

¹ 上海市经济和信息化委员会信息中心，上海 200125
² 复旦大学计算机科学技术学院，上海 201203
³ 上海市数据科学重点实验室，上海 201203

出版日期:2020-11-15 发布日期:2020-12-12
作者简介:王晓萍（1977- ），上海市经济和信息化委员会信息中心高级工程师，主要研究方向为大数据、政府信息化|郭梦洁（1994- ），复旦大学计算机科学技术学院、上海市数据科学重点实验室硕士生，主要研究方向为大数据、网络表示学习方法|岳婧雯（1997- ），复旦大学计算机科学技术学院、上海市数据科学重点实验室硕士生，主要研究方向为大数据、网络表示学习方法
基金资助:
国家自然科学基金资助项目(1636207);国家自然科学基金资助项目(6167115);上海市经济和信息化委员会市级部门预算资金项目(201859971904)

Research on person-position relationship based on relation graph

Xiaoping WANG¹,Mengjie GUO^2,³,Jingwen YUE^2,³

¹ Shanghai Municipal Commission of Economy and Informatization,Information Center,Shanghai 200125,China
² School of Computer Science,Fudan University,Shanghai 201203,China
³ Shanghai Key Laboratory of Data Science,Shanghai 201203,China

Online:2020-11-15 Published:2020-12-12
Supported by:
The National Natural Science Foundation of China(1636207);The National Natural Science Foundation of China(6167115);The Foundation of Shanghai Municipal Commission of Economy and Informatization(201859971904)

摘要/Abstract

摘要：

利用现有的数据进一步挖掘分析并帮助干部组织工作，是一个既有挑战又具有潜力的方向。针对干部信息数据的特点，使用基于关系图谱的人岗相宜研判方法分析领导班子，通过整合干部履历表以及人员基本信息库中的多源信息，构建关系图谱；将基于网络表示学习算法提取的关系图谱中的节点及关系等信息作为特征并输入分类模型，实现人岗关系研判。实验结果表明，基于关系图谱的方法可以很好地捕获人员和岗位之间的复杂关系信息，准确地判断人岗关系。

关键词: 人岗关系分析, 关系图谱, 网络表示学习, 大数据

Abstract:

Utilizing the existing data to further analyze and help leaders organize their work is a potential and challenging direction.According to the characteristics of leader information data,the leadership team was analyzed using the person-position relationship judgment method based on relation graph,a relation graph was built by integrating the leader resume and the multi-source information from the database.Then the information such as nodes and relationships in the relation graph extracted by the network representation learning method was used as features to input into the classification model.By using the proposed model,the relationship between people and positions can be inferred.The experimental results show that the method based on relation graph can well capture the complex relationship between people and positions,and can accurately judge the person-position relationship.

Key words: analysis on person-position relationship, relation graph, network representation learning, big data

中图分类号:

TP311

王晓萍,郭梦洁,岳婧雯. 基于关系图谱的人岗关系研究[J]. 大数据, 2020, 6(6): 0-.

Xiaoping WANG,Mengjie GUO,Jingwen YUE. Research on person-position relationship based on relation graph[J]. Big Data Research, 2020, 6(6): 0-.

图/表 9

图1

图2

图3

表1

节点与关系的数据统计"

节点类型	数量/个	关系类型	数量/个
S	22 536	$S \overset{e m p l o y e d}{\leftrightarrow} E$	7 590
O	30	$S \overset{p o s i t i o n}{\leftrightarrow} O$	4 144
E	1 646	$S \overset{r e l a t i v e}{\leftrightarrow} S$	882
D	73	$S \overset{g r a d u a t e}{\leftrightarrow} U$	1 326
I	8 427	$S \overset{w o r k}{\leftrightarrow} I$	55 669
U	377	$S \overset{b u \sin e s s}{\leftrightarrow} D$	1 728

表1

表2

表3

表4

AMinerCS 异质网络数据统计"

节点类型	数量/个	关系类型	数量/个
A	1 693 531	$P \overset{p u b l i s h}{\leftrightarrow} C$	3 194 405
P	3 194 405	$P \overset{w r i t t e n}{\leftrightarrow} A$	9 323 739
C	3 883

表4

表5

图4

参考文献 21

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评价指标	训练集	DWPPR		NRL4PPR
评价指标	训练集	DWPPR	OSESO	OSO	OSUSO
准确率	20%	0.841	0.869	0.868	0.868
	40%	0.838	0.871	0.872	0.871
	60%	0.837	0.871	0.872	0.871
	80%	0.831	0.874	0.876	0.875
AUC	20%	0.793	0.847	0.849	0.844
	40%	0.794	0.836	0.849	0.843
	60%	0.783	0.842	0.847	0.844
	80%	0.766	0.835	0.838	0.829

评价指标	训练集	DWPPR		NRL4PPR
评价指标	训练集	DWPPR	ISESI	ISI	ISUSI
准确率	20%	0.864	0.872	0.892	0.870
	40%	0.869	0.876	0.900	0.871
	60%	0.869	0.879	0.901	0.873
	80%	0.867	0.877	0.902	0.873
AUC	20%	0.761	0.814	0.871	0.802
	40%	0.777	0.830	0.884	0.809
	60%	0.787	0.833	0.890	0.818
	80%	0.788	0.832	0.894	0.822

评价指标		准确率				AUC
训练集	20%	40%	60%	80%	20%	40%	60%	80%
NRL4PPR （CPC）	0.853	0.855	0.855	0.854	0.800	0.801	0.802	0.803

基于关系图谱的人岗关系研究

Research on person-position relationship based on relation graph

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