NativE: Multi-modal Knowledge Graph Completion in the Wild

TL;DR

NativE introduces a novel framework for multi-modal knowledge graph completion that effectively handles imbalanced and diverse modality information in real-world datasets, achieving state-of-the-art results.

Contribution

The paper proposes NativE, a comprehensive framework with relation-guided dual adaptive fusion and collaborative modality adversarial training for wild multi-modal knowledge graphs.

Findings

Outperforms 21 baselines across multiple datasets

Achieves state-of-the-art performance in diverse scenarios

Efficient and generalizable approach

Abstract

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling the triple structure and multi-modal information from entities. However, real-world MMKGs present challenges due to their diverse and imbalanced nature, which means that the modality information can span various types (e.g., image, text, numeric, audio, video) but its distribution among entities is uneven, leading to missing modalities for certain entities. Existing works usually focus on common modalities like image and text while neglecting the imbalanced distribution phenomenon of modal information. To address these issues, we propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities and employs a collaborative modality adversarial training framework to augment the imbalanced modality information. We construct a new benchmark called WildKGC with five datasets to evaluate our method. The empirical results compared with 21 recent baselines confirm the superiority of our method, consistently achieving state-of-the-art performance across different datasets and various scenarios while keeping efficient and generalizable. Our code and data are released at https://github.com/zjukg/NATIVE