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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (1): 63-75    DOI: 10.11925/infotech.2096-3467.2019.0505
Current Issue | Archive | Adv Search |
Knowledge Representation Based on Deep Learning:Network Perspective
Chuanming Yu1(),Haonan Li2,Manyi Wang2,Tingting Huang2,Lu An3
1School of Information and Security Engineering, Zhongnan University of Economics and Law,Wuhan 430073, China
2School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
3School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper explores better representation models for the semantic relationship among knowledge objects.[Methods] Based on the existing algorithm of network representation learning, we proposed a combined knowledge network representation learning model (CKNRL), with integrated learning and deep learning techniques.[Results] We examined our new model with the knowledge network link prediction task of Chinese and English news parallel corpus. The AUC value of the CKNRL model was 0.929, which was higher than those of the traditional algorithms, i.e. DeepWalk(0.925), Node2Vec(0.926) and SDNE(0.899).[Limitations] Our study was based on the word co-occurrence network, and more research is needed to examine the CKNRL model for link prediction on more types of knowledge networks.[Conclusions] The semantic relationship among knowledge objects can be better represented by the proposed fusion model.

Key wordsKnowledge Representation      Deep Learning      Network Representation Learning      Link Prediction     
Received: 14 May 2019      Published: 14 March 2020
ZTFLH:  TP391  
Corresponding Authors: Chuanming Yu     E-mail: yucm@zuel.edu.cn

Cite this article:

Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An. Knowledge Representation Based on Deep Learning:Network Perspective. Data Analysis and Knowledge Discovery, 2020, 4(1): 63-75.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0505     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I1/63

符号表示 说明
di DeepWalk算法获得的针对网络中第i个节点的表征
ni Node2Vec算法获得的针对网络中第i个节点的表征
si SDNE算法获得的针对网络中第i个节点的表征
Dj DeepWalk算法针对第j个节点对的分类结果(概率)
Nj Node2Vec算法针对第j个节点对的分类结果(概率)
Sj SDNE算法针对第j个节点对的分类结果(概率)
Description of Related Symbols
Process of the CKNRL Model
网络信息 中文网络
名词性节点 3 132
动词性节点 1 956
形容词性节点 342
其他知识节点 50
边数 110 301
Statistics of the Single Language Knowledge Network
相关操作 详细说明
表示学习算法种类 DeepWalk、Node2Vec、SDNE
完成任务 链接预测
数据不平衡比例 正例:负例=1:3
选取变量 网络嵌入维度大小
滑动窗口大小
特征构造方法
模型融合方式
机器学习算法
训练集和测试比例 8:2
评估指标 Precision、Recall、F1、Accuracy、AUC
机器学习算法 XGBoost、LightGBM、NB、LR、MLP、RF
Description of Deep Representation Learning Experiments
算法 参数名 参数值
DeepWalk 迭代次数 80
随机游走长度 40
嵌入维度 50、100、150、200
Node2Vec 迭代次数 100
随机游走长度 80
嵌入维度 50、100、150、200
p 1
q 0.5
SDNE 迭代次数 300
学习率 0.01
批处理样本数 64
嵌入维度 50、100、150、200
Alpha 100
Gamma 1
Beta 10
XGBoost Thread 5
scale_pos_weight 3
Parameters of Each Representation Learning Algorithm
维度大小 Precision Recall F1 Accuracy AUC
50 0.74 0.69 0.72 0.864 0.912
100 0.79 0.69 0.74 0.876 0.917
150 0.78 0.69 0.73 0.873 0.915
200 0.75 0.72 0.73 0.870 0.912
Experimental Results of Link Prediction Tasks with Different Embedding Dimensions
窗口大小 Precision Recall F1 Accuracy AUC
3 0.80 0.53 0.63 0.848 0.869
5 0.79 0.69 0.74 0.876 0.917
7 0.75 0.73 0.74 0.871 0.921
9 0.74 0.77 0.75 0.875 0.928
Experimental Results of Link Prediction Tasks with Different Window Sizes
特征构造方法 Precision Recall F1 Accuracy AUC
拼接 0.63 0.78 0.69 0.828 0.891
点乘 0.69 0.78 0.74 0.859 0.919
相减取绝对值 0.74 0.76 0.75 0.872 0.927
相加取平均 0.54 0.73 0.62 0.775 0.828
相减取平方 0.73 0.77 0.75 0.872 0.927
Impact of Different Feature Construction Methods on Link Prediction
α β γ Precision Recall F1 AUC
0.0 0.0 1.0 0.65 0.77 0.71 0.899
0.0 0.3 0.7 0.67 0.74 0.70 0.896
0.0 0.6 0.4 0.59 0.71 0.64 0.861
0.0 0.9 0.1 0.73 0.76 0.75 0.925
0.1 0.0 0.9 0.67 0.77 0.72 0.906
0.1 0.3 0.6 0.67 0.74 0.70 0.893
0.1 0.6 0.3 0.64 0.73 0.68 0.885
0.1 0.9 0.0 0.74 0.77 0.75 0.929
0.2 0.0 0.8 0.67 0.77 0.72 0.905
0.2 0.3 0.5 0.65 0.73 0.69 0.880
0.2 0.6 0.2 0.69 0.75 0.72 0.908
0.3 0.0 0.7 0.68 0.76 0.72 0.901
0.3 0.3 0.4 0.60 0.70 0.65 0.857
0.3 0.6 0.1 0.71 0.77 0.74 0.917
0.4 0.1 0.5 0.66 0.73 0.69 0.884
0.4 0.4 0.2 0.66 0.75 0.70 0.897
0.5 0.0 0.5 0.64 0.72 0.68 0.877
0.5 0.3 0.2 0.64 0.75 0.69 0.896
0.6 0.0 0.4 0.60 0.72 0.66 0.862
0.6 0.3 0.1 0.70 0.78 0.73 0.917
0.7 0.1 0.2 0.69 0.77 0.73 0.911
0.8 0.0 0.2 0.70 0.77 0.73 0.915
0.9 0.0 0.1 0.71 0.79 0.75 0.922
1.0 0.0 0.0 0.72 0.78 0.75 0.925
Partial Experimental Results of Link Prediction Tasks with Different Model Fusion Methods (Network Embedding Fusion)
λ μ η Precision Recall F1 AUC
0.0 0.0 1.0 0.50 0.90 0.64 0.898
0.0 0.5 0.5 0.54 0.93 0.68 0.925
0.0 1.0 0.0 0.55 0.93 0.69 0.928
0.1 0.0 0.9 0.51 0.91 0.65 0.905
0.1 0.5 0.4 0.55 0.93 0.69 0.929
0.2 0.0 0.8 0.52 0.91 0.66 0.911
0.2 0.5 0.3 0.56 0.94 0.70 0.932
0.3 0.0 0.7 0.53 0.91 0.67 0.916
0.3 0.5 0.2 0.57 0.94 0.71 0.934
0.3 0.6 0.1 0.57 0.94 0.71 0.935
0.4 0.0 0.6 0.53 0.91 0.67 0.920
0.4 0.5 0.1 0.58 0.94 0.72 0.935
0.5 0.0 0.5 0.54 0.92 0.68 0.922
0.5 0.5 0.0 0.58 0.94 0.72 0.934
0.6 0.0 0.4 0.55 0.92 0.69 0.924
0.7 0.0 0.3 0.56 0.92 0.70 0.926
0.7 0.1 0.2 0.57 0.93 0.70 0.928
0.8 0.0 0.2 0.57 0.92 0.70 0.926
0.9 0.0 0.1 0.57 0.92 0.70 0.925
1.0 0.0 0.0 0.57 0.92 0.70 0.924
Partial Experimental Results of Link Prediction Tasks with Different Model Fusion Methods (Classification Result Fusion)
Algorithm Precision Recall F1 AUC
NB 0.74 0.76 0.75 0.929
LR 0.75 0.75 0.75 0.928
XGBoost 0.69 0.82 0.75 0.927
LightGBM 0.75 0.75 0.75 0.925
MLP 0.75 0.73 0.74 0.914
RF 0.66 0.77 0.71 0.903
Bagging 0.66 0.73 0.69 0.893
BVC 0.67 0.75 0.71 0.903
Voting 0.78 0.74 0.76 0.924
Impact of Machine Learning Algorithms on Link Prediction (Network Embedding Fusion)
Algorithm Precision Recall F1 AUC
RF 0.70 0.77 0.73 0.917
LR 0.79 0.72 0.75 0.935
MLP 0.79 0.69 0.74 0.918
XGBoost 0.71 0.84 0.77 0.937
LightGBM 0.78 0.74 0.76 0.936
NB 0.78 0.76 0.77 0.920
Bagging 0.72 0.75 0.73 0.920
Voting 0.78 0.75 0.76 0.933
BVC 0.72 0.78 0.75 0.924
Impact of Machine Learning Algorithms on Link Prediction (Classification Result Fusion)
方法 Precision Recall F1 Accuracy AUC
CKNRL 0.74 0.77 0.75 0.874 0.929
DeepWalk 0.72 0.78 0.75 0.868 0.925
Node2Vec 0.73 0.77 0.75 0.872 0.926
SDNE 0.65 0.77 0.71 0.840 0.899
Represents Learning Comparison Experiment Results
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