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- W3174009783 abstract "We propose a self-supervised embedding learning frameworkSelfLinKGto link concepts in heterogeneous knowledge graphs. Without any labeled data, SelfLinKG can achieve competitive performance against its supervised counterpart, and significantly outperforms state-of-the-art unsupervised methods by 26%-50%. The essential components of SelfLinKG are local attention-based encoding and momentum contrastive learning. The former aims to learn the graph representation using an attention network, while the latter is to learn a self-supervised model across knowledge graphs using contrastive learning. SelfLinKG has been deployed to build the the new version, called OAG_know of Open Academic Graph (OAG). All data and codes are publicly available." @default.
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- W3174009783 date "2021-01-01" @default.
- W3174009783 modified "2023-10-18" @default.
- W3174009783 title "OAG_know: Self-supervised Learning for Linking Knowledge Graphs" @default.
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- W3174009783 doi "https://doi.org/10.1109/tkde.2021.3090830" @default.
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