Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion

TL;DR

This paper introduces MKGformer, a hybrid transformer model with multi-level fusion designed for diverse multimodal knowledge graph completion tasks, achieving state-of-the-art results across multiple datasets.

Contribution

The paper proposes a unified hybrid transformer architecture with multi-level fusion for various multimodal knowledge graph completion tasks, addressing modality relevance and task diversity.

Findings

Achieves SOTA performance on four multimodal datasets

Effectively integrates visual and text features via multi-level fusion

Demonstrates versatility across link prediction, relation extraction, and NER

Abstract

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have recently been successfully applied to tasks such as information retrieval, question answering, and recommendation system. Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction. However, different tasks and modalities require changes to the model architecture, and not all images/objects are relevant to text input, which hinders the applicability to diverse real-world scenarios. In this paper, we propose a hybrid transformer with multi-level fusion to address those issues. Specifically, we leverage a hybrid transformer architecture with unified input-output for diverse multimodal knowledge graph completion tasks. Moreover, we propose multi-level fusion, which integrates visual and text representation via coarse-grained prefix-guided interaction and fine-grained correlation-aware fusion modules. We conduct extensive experiments to validate that our MKGformer can obtain SOTA performance on four datasets of multimodal link prediction, multimodal RE, and multimodal NER. Code is available in https://github.com/zjunlp/MKGformer.