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    <abstract>Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://github.com/OreOZhao/MoSE4MKGC.</abstract>
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%0 Conference Proceedings
%T MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion
%A Zhao, Yu
%A Cai, Xiangrui
%A Wu, Yike
%A Zhang, Haiwei
%A Zhang, Ying
%A Zhao, Guoqing
%A Jiang, Ning
%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 zhao-etal-2022-mose
%X Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://github.com/OreOZhao/MoSE4MKGC.
%R 10.18653/v1/2022.emnlp-main.719
%U https://aclanthology.org/2022.emnlp-main.719
%U https://doi.org/10.18653/v1/2022.emnlp-main.719
%P 10527-10536

Markdown (Informal)

[MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion](https://aclanthology.org/2022.emnlp-main.719) (Zhao et al., EMNLP 2022)

• MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion (Zhao et al., EMNLP 2022)

ACL

• Yu Zhao, Xiangrui Cai, Yike Wu, Haiwei Zhang, Ying Zhang, Guoqing Zhao, and Ning Jiang. 2022. MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10527–10536, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.