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KGEL: A novel end-to-end embedding learning framework for knowledge graph completion
Adnan Zeb1; Anwar Ul Haq1; Defu Zhang1; Junde Chen1; Zhiguo Gong2,3
2021-04
Source PublicationExpert Systems with Applications
ABS Journal Level1
ISSN0957-4174
Volume167
Abstract

Knowledge graphs (KGs) have recently become increasingly popular due to the broad range of essential applications in various downstream tasks including intelligent search, personalized recommendations, intelligent financial data analytics, etc. During an automated construction of a KG, the knowledge facts from multiple knowledge sources are automatically extracted in the form of triples, and these observed triples are used to derive new unobserved triples for KG completion (also known as link prediction). State-of-the-art link prediction methods are known to be primarily KG embedding models, among which tensor factorization models have recently drawn much attention due to their scalability and expressive feature embeddings, and hence, perform well for link prediction. However, these embedding models consider each KG triple individually and fail to capture the useful information present in the neighborhood of a node. To this end, we propose a novel end-to-end KG embedding learning framework that consists of an encoder of a dual weighted graph convolutional network, and a decoder of a novel fully expressive tensor factorization model. The proposed encoder extends weighted graph convolutional network to generate two rich and high quality embedding vectors for each node by aggregating information from the neighboring nodes. The proposed decoder has a flexible and powerful tensor representation form of the Tensor Train decomposition that takes benefit of the two representations of each node in its embedding space to accurately model the KG triples. We also derive a bound on the size of the embeddings for full expressivity and show that our proposed tensor factorization model is fully expressive. Additionally, we show the relationship of our tensor factorization model to previous tensor factorization models. The experimental results show the effectiveness of the proposed framework that consistently marks performance gains over several previous models on recent standard link prediction datasets.

KeywordKnowledge Graph Link Prediction Tensor Factorization Tensor Train Decomposition Weighted Graph Convolutional Network
DOI10.1016/j.eswa.2020.114164
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering ; Operations Research & Management Science
WOS SubjectComputer Science, Artificial Intelligenceengineering, Electrical & Electronicoperations Research & Management Science
WOS IDWOS:000623561000003
Scopus ID2-s2.0-85094876791
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Citation statistics
Cited Times [WOS]:5   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorDefu Zhang
Affiliation1.School of Informatics,Xiamen University,Fujian,361005,China
2.The State Key Laboratory of Internet of Things for Smart City,University of Macau,Macau,China
3.Department of Computer and Information Science,University of Macau,Macau,China
Recommended Citation
GB/T 7714
Adnan Zeb,Anwar Ul Haq,Defu Zhang,et al. KGEL: A novel end-to-end embedding learning framework for knowledge graph completion[J]. Expert Systems with Applications,2021,167.
APA Adnan Zeb,Anwar Ul Haq,Defu Zhang,Junde Chen,&Zhiguo Gong.(2021).KGEL: A novel end-to-end embedding learning framework for knowledge graph completion.Expert Systems with Applications,167.
MLA Adnan Zeb,et al."KGEL: A novel end-to-end embedding learning framework for knowledge graph completion".Expert Systems with Applications 167(2021).
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