Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding
This addresses a specific bottleneck in multi-modal knowledge graph embedding for researchers and practitioners, though it appears incremental.
The paper tackles the problem of negative sampling in multi-modal knowledge graph embedding by proposing Modality-Aware Negative Sampling (MANS), which aligns structural and visual embeddings and outperforms existing methods on two benchmarks.
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.