Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.

翻译：多模态知识图谱（MMKG）通过整合多种模态数据（如文本和图像）实现对实体的全面理解。尽管大规模多模态知识图谱近年取得进展，现有MMKG忽视了实体的多方面特性，限制了从不同视角理解实体的能力。本文构建了AspectMMKG——首个通过将图像匹配至不同实体方面来实现方面关联图像的MMKG。具体而言，我们从知识库中采集方面关联图像，并进一步从知识库中提取方面关联句子作为查询，通过在线图像搜索引擎检索大量方面关联图像。最终AspectMMKG包含2,380个实体、18,139个实体方面以及645,383张方面关联图像。我们验证了AspectMMKG在实体方面链接（EAL）下游任务中的可用性，表明借助AspectMMKG，现有EAL模型达到了新的最优性能。为促进方面关联MMKG的研究，我们进一步提出方面关联图像检索（AIR）模型，旨在修正并扩展AspectMMKG中的方面关联图像。通过融合实体图像、方面及方面图像信息，我们训练AIR模型学习实体图像与实体方面关联图像之间的关系。实验结果表明，AIR模型能够为给定实体检索到针对不同方面的合适图像。