Papers with KG completion methods

3 papers
Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network (2021.acl-long)

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Challenge: Knowledge graphs (KGs) are incomplete because of the large number of benchmark datasets that are not representative of real KGs.
Approach: They develop a deep convolutional network that utilizes textual entity representations to distill the knowledge from the convolution into a student network that re-ranks promising candidate entities.
Outcome: The proposed model outperforms recent methods in a realistic setting where dense connectivity is not guaranteed.
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)

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Challenge: Existing knowledge graphs are incomplete and lack the order of relations in paths.
Approach: They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning.
Outcome: The proposed method performs better than state-of-the-art methods on two benchmark datasets.
Joint Multilingual Knowledge Graph Completion and Alignment (2022.findings-emnlp)

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Challenge: Existing work on multilingual KG completion has focused on entity and relation alignments, but understanding of how it can aid multilingual alignments is limited.
Approach: They propose to combine two components that jointly accomplish KG completion and alignment.
Outcome: The proposed model outperforms existing competitive baselines on a public multilingual benchmark and achieves state-of-the-art results.

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