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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| Challenge: | Existing methods to encode and match entity pairs have only a few observed reference entity pairs. |
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Vinh Tong, Dat Quoc Nguyen, Trung Thanh Huynh, Tam Thanh Nguyen, Quoc Viet Hung Nguyen, Mathias Niepert
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| Challenge: | Existing few-shot learning-based models have difficulty alleviating the long-tail issue on low-resource KGs because of the lack of training tasks. |
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