Out-of-Sample Representation Learning for Knowledge Graphs (2020.findings-emnlp)
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| Challenge: | Existing work considers attributed graphs for transductive reasoning, but this problem is under-explored for non-attributed graph. |
| Approach: | They propose to use attributed and non-attributed graphs to solve an out-of-sample representation learning problem for non-credited knowledge graphs. |
| Outcome: | The proposed model and baselines compare with existing models and baseline models. |
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