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Learning graph attention-aware knowledge graph embedding
Li, Chen1; Peng, Xutan6; Niu, Yuhang1,2; Zhang, Shanghang3; Peng, Hao1,5; Zhou, Chuan4; Li, Jianxin1
Source PublicationNEUROCOMPUTING
AbstractThe knowledge graph, which utilizes graph structure to represent multi-relational data, has been widely used in the reasoning and prediction tasks, attracting considerable research efforts recently. However, most existing works still concentrate on learning knowledge graph embeddings straightforwardly and intuitively without subtly considering the context of knowledge. Specifically, recent models deal with each single triple independently or consider contexts indiscriminately, which is one-sided as each knowledge unit (i.e., triple) can be derived from its partial surrounding triples. In this paper, we propose a graph-attention-based model to encode entities, which formulates a knowledge graph as an irregular graph and explores a number of concrete and interpretable knowledge compositions by integrating the graph-structured information via multiple independent channels. To measure the correlation between entities from different angles (i.e., entity pair, relation, and structure), we respectively develop three attention metrics. By making use of our enhanced entity embeddings, we further introduce several improved factorization functions for updating relation embeddings and evaluating candidate triples. We conduct extensive experiments on downstream tasks including entity classification, entity typing, and link prediction to validate our methods. Empirical results validate the importance of our introduced attention metrics and demonstrate that our proposed method can improve the performance of factorization models on large-scale knowledge graphs. (c) 2021 Elsevier B.V. All rights reserved.
KeywordKnowledge graph embedding Graph attention mechanism Entity typing Link prediction
Indexed BySCI
Funding ProjectNatural Science Foundation of China program[U20B2053] ; Natural Science Foundation of China program[62002007] ; Natural Science Foundation of China program[61772151] ; S&T Program of Hebei[20310101D] ; S&T Program of Hebei[SKLSDE-2020ZX-12]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000704086300003
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Document Type期刊论文
Corresponding AuthorLi, Chen
Affiliation1.Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing, Peoples R China
2.Beihang Univ, State Key Lab Software Dev Environm Lab, Beijing, Peoples R China
3.Carnegie Mellon Univ, Dept Elect & Comp Engn, Pittsburgh, PA USA
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
5.Beihang Univ, Sch Cyber Sci & Technol, Beijing, Peoples R China
6.Univ Sheffield, Dept Comp Sci, Sheffield, S Yorkshire, England
Recommended Citation
GB/T 7714
Li, Chen,Peng, Xutan,Niu, Yuhang,et al. Learning graph attention-aware knowledge graph embedding[J]. NEUROCOMPUTING,2021,461:516-529.
APA Li, Chen.,Peng, Xutan.,Niu, Yuhang.,Zhang, Shanghang.,Peng, Hao.,...&Li, Jianxin.(2021).Learning graph attention-aware knowledge graph embedding.NEUROCOMPUTING,461,516-529.
MLA Li, Chen,et al."Learning graph attention-aware knowledge graph embedding".NEUROCOMPUTING 461(2021):516-529.
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