Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction. However, different tasks and modalities require changes to the model architecture, and not all images/objects are relevant to text input, which hinders the applicability to diverse real-world scenarios. In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. Specifically, we leverage a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks. Moreover, we propose multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine-grained correlation-aware fusion modules. We conduct extensive experiments to validate that our MKGformer can obtain SOTA performance on four datasets of multimodal link prediction, multimodal RE, and multimodal NER. Code is available in https://github.com/zjunlp/MKGformer.
翻译:组织视觉文字事实知识的多式知识图集(MKGs)组织视觉文字事实知识,最近被成功地应用于信息检索、问答和建议系统等任务。由于大多数MKGs远未完成,因此提出了广泛的知识图集完成研究,重点是多式联运实体、关系提取和链接预测。然而,不同的任务和模式要求改变模型结构,并非所有图像/对象都与文本输入有关,这妨碍了对不同现实世界情景的适用性。在本文件中,我们提议了一种混合变压器,具有多级聚合,以解决这些问题。具体地说,我们利用一种混合变压器结构,具有统一的输入-输出,以完成多种多式联运知识图集的任务。此外,我们提议了多级的组合,通过粗微的预导式互动和精细的感光度相关感应变聚模块将视觉和文字代表整合在一起。我们进行了广泛的实验,以证实我们的MKGrew能够取得四套数据集的SOTA性表现、多式联运连接预测、多式RE和多式NER。代码可在 https://github.com/zgrustriveG/zjunpl.code。