A large number of studies have emerged for Multimodal Knowledge Graph
Completion (MKGC) to predict the missing links in MKGs. However, fewer studies
have been proposed to study the inductive MKGC (IMKGC) involving emerging
entities unseen during training. Existing inductive approaches focus on
learning textual entity representations, which neglect rich semantic
information in visual modality. Moreover, they focus on aggregating structural
neighbors from existing KGs, which of emerging entities are usually limited.
However, the semantic neighbors are decoupled from the topology linkage and
usually imply the true target entity. In this paper, we propose the IMKGC task
and a semantic neighbor retrieval-enhanced IMKGC framework CMR, where the
contrast brings the helpful semantic neighbors close, and then the memorize
supports semantic neighbor retrieval to enhance inference. Specifically, we
first propose a unified cross-modal contrastive learning to simultaneously
capture the textual-visual and textual-textual correlations of query-entity
pairs in a unified representation space. The contrastive learning increases the
similarity of positive query-entity pairs, therefore making the representations
of helpful semantic neighbors close. Then, we explicitly memorize the knowledge
representations to support the semantic neighbor retrieval. At test time, we
retrieve the nearest semantic neighbors and interpolate them to the
query-entity similarity distribution to augment the final prediction. Extensive
experiments validate the effectiveness of CMR on three inductive MKGC datasets.
Codes are available at this https URL

提出了一种基于语义邻居检索增强的归纳式多模态知识图谱补全框架 CMR，通过对多模态查询实体对的文本 - 视觉和文本 - 文本相关性进行统一的交叉模态对比学习，增加有助于语义邻居的相似性，然后显式记忆知识表示以支持语义邻居检索，并在测试时通过插值将最近的语义邻居与查询实体的相似性分布相结合以增强最终预测，实验证明了 CMR 在三个归纳式多模态知识图谱补全数据集上的有效性。

对比即记忆：语义邻居检索增强的归纳式多模态知识图谱补全

Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive  Multimodal Knowledge Graph Completion

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
this https URL.

本文中提出的 MoSE 框架通过学习分解关系嵌入和集成预测结果，成功地解决了不同模式下关系表示共享的问题，以及不同模式下关系重要性的动态建模问题。