  Multi-modal knowledge graphs (MKGs) include not only the relation triplets,
but also related multi-modal auxiliary data (i.e., texts and images), which
enhance the diversity of knowledge. However, the natural incompleteness has
significantly hindered the applications of MKGs. To tackle the problem,
existing studies employ the embedding-based reasoning models to infer the
missing knowledge after fusing the multi-modal features. However, the reasoning
performance of these methods is limited due to the following problems: (1)
ineffective fusion of multi-modal auxiliary features; (2) lack of complex
reasoning ability as well as inability to conduct the multi-hop reasoning which
is able to infer more missing knowledge. To overcome these problems, we propose
a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning).
Specifically, the model contains the following two components: (1) a unified
gate-attention network which is designed to generate effective multi-modal
complementary features through sufficient attention interaction and noise
reduction; (2) a complementary feature-aware reinforcement learning method
which is proposed to predict missing elements by performing the multi-hop
reasoning process, based on the features obtained in component (1). The
experimental results demonstrate that MMKGR outperforms the state-of-the-art
approaches in the MKG reasoning task.
