Titlebook: Database Systems for Advanced Applications; 28th International C Xin Wang,Maria Luisa Sapino,Hongzhi Yin Conference proceedings 2023 The Ed

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https://doi.org/10.1007/978-3-031-30678-5Query Processing; Data Management; Graph; Network; Knowledge Graph

挖掘

978-3-031-30677-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

猜忌

Christian Masiak,Alexandra Moritz,Frank Langt of the existing approaches can hardly model complicated contexts since they fail to use dependency-type knowledge in texts to assist in identifying implicit clues to event relations, leading to the sub-optimal performance on this task. To this end, we propose a novel type-guided attentive graph co

Mosaic

ENACT

Contemporary Ecology Research in Chinaticles into events and connect related events in growing trees to generate storylines. Unfortunately, these methods did not perform well in learning the implicit associations of events. More recently, Graph Convolutional Network (GCN) based methods are proposed to learn the implicit associations bet

ENACT

caldron

小争吵

外露

Corporate Ethics and Management Theoryning has been used in UDA, which exploits pseudo-labels for unlabeled target domains. However, the pseudo-labels can be unreliable due to distribution shifts between domains, severely impairing the model performance. To address this problem, we propose a novel self-training framework-Self-Training w