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OAGknow: Self-Supervised Learning for Linking Knowledge Graphs
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DOI：10.1109/TKDE.2021.3090830.png)
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摘要
En 中文
propose a self-supervised embedding learning framework-SelfLinKG-to 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% under linear classification protocol. 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.
Keyword:
Encyclopedias
Internet
Electronic publishing
Knowledge based systems
Taxonomy
Training
Encoding
Concept linking
self-supervised learning
contrastive learning
knowledge base
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10.4
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