Mixed-Curvature Multi-Modal Knowledge Graph Completion

Multi-modal Knowledge Graph Completion (KGC), which aims to enrich knowledge graph embeddings by incorporating images and text as supplementary information alongside triplets, is an significant task in learning KGs. Existing multi-modal KGC methods mainly focus on modalitylevel fusion, neglecting the importance of modeling the complex structures, such as hierarchical and circular patterns. To address this, we propose a Mixed-Curvature multi-modal Knowledge Graph Completion method (MCKGC) that embeds the information into three single-curvature spaces, including hyperbolic space, hyperspherical space, and Euclidean space, and incorporates multi-modal information into a mixed space. Specifically, MCKGC consists of Modality Information Mixed-Curvature Module (MIMCM) and Progressive Fusion Module (PFM). To improve the expressive ability for different modalities, MIMCM introduces multi-modal information into three single-curvature spaces for interaction. Then, to extract useful information from different modalities and capture the complex structure from the geometric information, PFM implements a progressive fusion strategy by utilizing modality-level and space-level gates to adaptively incorporate the information from different spaces. Extensive experiments on three widely used benchmarks demonstrate the effectiveness of our method.

Authors

• Yuxiao Gao Institute of Artificial In telligence, Beihang University
• Fuwei Zhang Institute of Artificial In telligence, Beihang University
• Zhao Zhang Hefei University of Technology, Yunnan Key Laboratory of Software Engineering
• Xiaoshuang Min The Sixth Research Institute of China Electronics Corporation
• Fuzhen Zhuang Institute of Artificial In telligence, Beihang University Zhongguancun Laboratory

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