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- W3179332483 abstract "We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion (i.e., link prediction). Given a knowledge graph, NoGE constructs a single graph considering entities and relations as individual nodes. NoGE then computes weights for edges among nodes based on the co-occurrence of entities and relations. Next, NoGE proposes Dual Quaternion Graph Neural Networks (Dual-QGNN) and utilizes Dual-QGNN to update vector representations for entity and relation nodes. NoGE then adopts a score function to produce the triple scores. Comprehensive experimental results show that NoGE obtains state-of-the-art results on three new and difficult benchmark datasets CoDEx for knowledge graph completion." @default.
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- W3179332483 date "2021-04-15" @default.
- W3179332483 modified "2023-09-22" @default.
- W3179332483 title "Node Co-occurrence based Dual Quaternion Graph Neural Networks for Knowledge Graph Link Prediction" @default.
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