Papers with candidate triple evaluation

1 papers
Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)

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Challenge: Existing knowledge graph completion models only evaluated candidate triples from content information.
Approach: They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation.
Outcome: The proposed model improves on two representative datasets and improves performance.

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