  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.
