Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts
Shuai Wang1,2; Chenchen Huang1,2; Juanjuan Li1,3; Yong Yuan1,3; Fei-Yue Wang1,2,3
Source PublicationIEEE Access
ISSN2169-3536
2019
Volume7Issue:Sep. 2019Pages:136951- 136961
Corresponding AuthorYuan, Yong(yong.yuan@ia.ac.cn)
Subtype期刊文章
Abstract

Since first coined by Google in 2012, knowledge graph has received extensive attention from both industry and academia, and has been widely used in many scenarios with success, e.g. information retrieval, online recommendation, question-answering, and so on. However, traditional centralized construction of knowledge graph faces many challenges, such as laborious and time-consuming, vulnerable to manipulation or tampering, lacking scrutiny, among others. Therefore, in this paper, we propose a novel decentralized knowledge graph construction method by means of crowdsourcing, and the business logic of crowdsourcing is implemented by blockchain-powered smart contracts to guarantee the transparency, integrity, and auditability. On this basis, the decentralized knowledge graph is used for a deep recommender system, and case studies validate the effectiveness of the system. This paper is aimed at providing a novel decentralized approach for constructing knowledge graph and serving as reference and guidance for future research and practical applications of knowledge graph.

KeywordDecentralized Knowledge Graph Deep Recommender System Crowdsourcing Blockchain Smart Contracts
DOI10.1109/ACCESS.2019.2942338
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[71702182] ; National Key R&D Program of China[2018AAA0101400]
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000498703100002
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25804
Collection复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
Corresponding AuthorYong Yuan
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
3.青岛智能产业技术研究院
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shuai Wang,Chenchen Huang,Juanjuan Li,et al. Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts[J]. IEEE Access,2019,7(Sep. 2019):136951- 136961.
APA Shuai Wang,Chenchen Huang,Juanjuan Li,Yong Yuan,&Fei-Yue Wang.(2019).Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts.IEEE Access,7(Sep. 2019),136951- 136961.
MLA Shuai Wang,et al."Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts".IEEE Access 7.Sep. 2019(2019):136951- 136961.
Files in This Item: Download All
File Name/Size DocType Version Access License
Decentralized Constr(2505KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Shuai Wang]'s Articles
[Chenchen Huang]'s Articles
[Juanjuan Li]'s Articles
Baidu academic
Similar articles in Baidu academic
[Shuai Wang]'s Articles
[Chenchen Huang]'s Articles
[Juanjuan Li]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shuai Wang]'s Articles
[Chenchen Huang]'s Articles
[Juanjuan Li]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.