Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subject, relation, object).
Oct 30, 2018
Dec 7, 2018 · It outperformed several state-of-the-art KG completion models on the conventional entity prediction task for many evaluation metrics, based on ...
DSKG: A Deep Sequential Model for Knowledge Graph Completion
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A new model is proposed, which uses a KG-specific multi-layer recurrent neutral network (RNN) to model triples in aKG as sequences and is enabled by the ...
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subject, relation, object).
Learning to Complete Knowledge Graphs with Deep Sequential Models. Data Intelligence, 1(3):224–243, 2019.
Jun 1, 2019 · In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It ...
DSKG: A Deep Sequential Model for Knowledge Graph Completion. L. Guo, Q. Zhang, W. Ge, W. Hu, and Y. Qu. CCKS, volume 957 of Communications in Computer and ...
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subject, relation, object).
Article "DSKG: A Deep Sequential Model for Knowledge Graph Completion" Detailed information of the J-GLOBAL is an information service managed by the Japan ...
DSKG: A Deep Sequential Model for Knowledge Graph Completion ... Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is ...