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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (5): 48-59    DOI: 10.11925/infotech.2096-3467.2022.0424
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Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding
Li Kaijun1,Niu Zhendong1(),Shi Kaize1,2,Qiu Ping1
1School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China
2Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney 2007, Australia
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Abstract  

[Objective] This paper proposes a new model that integrates multiple features to provide accurate paper recommendation services for researchers. [Methods] First, we designed a feature extraction framework to extract and fuse entity relation features and topic features from the knowledge graph and the content of academic papers, respectively. Then, we proposed a paper recommendation method based on the knowledge embedding-based encoding-decoding model, which improved the learning effect of high-dimensional fusion features. [Results] We examined our new model on the DBLP-v11 dataset. The proposed method improved the Recall and MRR scores by 8.9% and 2.9%, respectively, compared with the suboptimal model. [Limitations] The proposed graph feature learning method does not consider the weight of entities in the real environment. [Conclusions] The new paper recommendation method could effectively learn high-dimensional features, which provide guidance for subsequent research.
Key words： Paper Recommendation      Academic Paper Knowledge Graph      Knowledge Embedding      Feature Fusion      Feature Learning     
Received: 04 May 2022      Published: 29 July 2022
ZTFLH:  TP391  
  G25  
Fund:National Key R&D Program of China(2019YFB1406303)
Corresponding Authors: Niu Zhendong，ORCID：0000-0002-0576-7572，E-mail：zniu@bit.edu.cn。   
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Kaijun Li
Zhendong Niu
Kaize Shi
Ping Qiu

Cite this article:

Li Kaijun, Niu Zhendong, Shi Kaize, Qiu Ping. Paper Recommendation Based on Academic Knowledge Graph and Subject Feature Embedding. Data Analysis and Knowledge Discovery, 2023, 7(5): 48-59.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0424     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I5/48

The Overview of KE-EDM Model
符号 描述 数量
P 学术论文 696 131
V 出版机构 4 083
Y 年份 5
A f 工作机构 600 266
A u 作者 1 015 371
共计 2 315 856
The Introduction of Entities
符号 描述
A u → P 作者写论文
P → V 论文刊载出版机构
A u → A f 作者所在工作机构
P → Y 学术论文出版时间
P → P 论文引用论文
A u → A u 作者与作者合作
The Introduction of Relationships
域 样本数据举例或基本描述
论文ID 1000018889
题目 Remote Policy Enforcement ···Execution in
Mobile Environments
作者信息 作者ID、姓名以及隶属工作机构
出版机构 出版机构 ID 以及名称
年份 2013
参考论文 本篇论文引用的论文 ID 组成的列表
摘要 Both in ···viableand effective.
The Dataset Metadata
Top N KE-EDM （本文） HKE-ARNN Citeomatic ClusCite VOPRec
查准率 查全率 F1
分数		 查准率 查全率 F1
分数		 查准率 查全率 F1
分数		 查准率 查全率 F1
分数		 查准率 查全率 F1
分数
1 0.686 0.337 0.452 0.654 0.062 0.113 0.469 0.039 0.072 0.412 0.019 0.036 0.313 0.053 0.091
5 0.516 0.406 0.454 0.511 0.162 0.246 0.348 0.140 0.200 0.352 0.152 0.212 0.187 0.160 0.172
10 0.265 0.463 0.337 0.281 0.291 0.286 0.294 0.235 0.261 0.242 0.212 0.226 0.159 0.280 0.203
20 0.150 0.682 0.247 0.160 0.593 0.252 0.222 0.354 0.273 0.195 0.300 0.236 0.135 0.410 0.203
50 0.064 0.705 0.117 0.067 0.596 0.120 0.139 0.539 0.221 0.111 0.427 0.176 0.059 0.460 0.105
Performance of KE-EDM and Baseline Methods
MRR Scores for Each Model
主题特征 Precision@20 Recall@20 F1分数 MRR
标题&摘要 0.151 0.682 0.247 0.687
标题 0.150 0.655 0.244 0.659
摘要 0.151 0.662 0.245 0.686
The Effects of Different Text Feature Dimension
Influence of Vector Features of Different Dimensions on Model Recommendation Performance
方法 Precision@20 Recall@20 F1分数 MRR
TransD 0.151 0.682 0.247 0.687
TransR 0.150 0.675 0.245 0.689
TransH 0.146 0.664 0.239 0.656
TransE 0.144 0.650 0.235 0.649
The Effects of Different Graph Feature Representation Learning Methods on Results
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