Papers with Few-shot knowledge graph completion

3 papers
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion (2021.findings-emnlp)

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Challenge: Existing methods to encode and match entity pairs have only a few observed reference entity pairs.
Approach: They propose a model that infers and leverages paths that can expressively encode the relation of two entities.
Outcome: The proposed model outperforms the state-of-the-art models by 11.2– 14.2% in terms of Hits@1.
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (2022.coling-1)

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Challenge: Existing knowledge graph completion models require only a few associative triples to complete a relationship.
Approach: They propose to perform data augmentation from two perspectives to solve the FKGC problem by inferring new triple facts from existing models.
Outcome: The proposed framework can be applied to a number of existing models.
Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion (2022.emnlp-main)

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Challenge: Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.
Approach: They propose a model to learn semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities.
Outcome: The proposed model outperforms state-of-the-art methods on two public datasets.

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