中文版 | English
Title

Zero-Shot Knowledge Graph Completion for Recommendation System

Author
Corresponding AuthorTang, Ke
DOI
Publication Years
2022
Conference Name
23rd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
ISSN
0302-9743
EISSN
1611-3349
ISBN
978-3-031-21752-4
Source Title
Volume
13756
Conference Date
NOV 24-26, 2022
Conference Place
null,Manchester,ENGLAND
Publication Place
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Publisher
Abstract
Knowledge graphs are structured representations of actual entities and relations. They are widely used to improve the performance of downstream tasks such as recommendation systems and semantic searching. Knowledge graph completion (KGC) is a technology for discovering the missing relations between the entities in a knowledge graph (KG). Existing methods leverage known relations on a KG to build a model to predict missing relations. Such methods implicitly require a substantial number of relations to be known in advance, which might not be available in practice. To cope with the cold-start scenario for KGC, i.e., no relation is known in advance, we propose a zero-shot approach in this paper. Our approach converts the KGC process to an optimization problem. It uses the Evolutionary Strategy (ES) algorithm to optimize a model used to complete the KG according to the performance of the recommendation system constructed based on the completed KG. Experiments on a movie dataset demonstrate that our approach can complete the KG in the cold-start scenario and improve the performance of the recommendation system built based on the completed KG.
Keywords
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Indexed By
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS Accession No
WOS:000904430900019
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/430693
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Key Lab Brain Inspired Intelligent Comp, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen 518055, Peoples R China
First Author AffilicationDepartment of Computer Science and Engineering
Corresponding Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Wang, Zhiyuan,Chen, Cheng,Tang, Ke. Zero-Shot Knowledge Graph Completion for Recommendation System[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022.
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