  Negative sampling (NS) is widely used in knowledge graph embedding (KGE),
which aims to generate negative triples to make a positive-negative contrast
during training. However, existing NS methods are unsuitable when multi-modal
information is considered in KGE models. They are also inefficient due to their
complex design. In this paper, we propose Modality-Aware Negative Sampling
(MANS) for multi-modal knowledge graph embedding (MMKGE) to address the
mentioned problems. MANS could align structural and visual embeddings for
entities in KGs and learn meaningful embeddings to perform better in
multi-modal KGE while keeping lightweight and efficient. Empirical results on
two benchmarks demonstrate that MANS outperforms existing NS methods.
Meanwhile, we make further explorations about MANS to confirm its
effectiveness.
