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        <title>Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion</title>
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        <namePart type="given">Yuling</namePart>
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        <namePart type="given">Kui</namePart>
        <namePart type="family">Yu</namePart>
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    <name type="personal">
        <namePart type="given">Xiaoling</namePart>
        <namePart type="family">Huang</namePart>
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    <name type="personal">
        <namePart type="given">Yuhong</namePart>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yoav</namePart>
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    <abstract>Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at https://github.com/cjlyl/FKGC-CIAN.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.524</identifier>
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%0 Conference Proceedings
%T Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion
%A Li, Yuling
%A Yu, Kui
%A Huang, Xiaoling
%A Zhang, Yuhong
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-learning-inter
%X Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at https://github.com/cjlyl/FKGC-CIAN.
%R 10.18653/v1/2022.emnlp-main.524
%U https://aclanthology.org/2022.emnlp-main.524
%U https://doi.org/10.18653/v1/2022.emnlp-main.524
%P 7691-7700

Markdown (Informal)

[Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion](https://aclanthology.org/2022.emnlp-main.524) (Li et al., EMNLP 2022)

• Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion (Li et al., EMNLP 2022)

ACL

• Yuling Li, Kui Yu, Xiaoling Huang, and Yuhong Zhang. 2022. Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7691–7700, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.