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VL-KGE: Vision-Language Models Meet Knowledge Graph Embeddings

arXiv:2603.02435v1 Announce Type: new Abstract: Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KGE) methods excel at learning continuous representations of entities and relations, yet they are typically designed for unimodal settings. Recent approaches extend KGE to multimodal settings but remain constrained, often processing modalities in isolation, resulting in weak cross-modal alignment, and relying on simplistic assumptions such as uniform modality availability across entities. Vision-Language Models (VLMs) offer a powerful way to align diverse modalities within a shared embedding space. We propose Vision-Language Knowledge Graph Embeddings (VL-KGE), a framework that integrates cross-modal alignment from VLMs with structured relational modeling to learn unified multimodal representations of knowledge graphs. Experiments on WN9-IMG and two novel fine ar

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Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring · · 1 min read · 3 views
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arXiv:2603.02435v1 Announce Type: new Abstract: Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KGE) methods excel at learning continuous representations of entities and relations, yet they are typically designed for unimodal settings. Recent approaches extend KGE to multimodal settings but remain constrained, often processing modalities in isolation, resulting in weak cross-modal alignment, and relying on simplistic assumptions such as uniform modality availability across entities. Vision-Language Models (VLMs) offer a powerful way to align diverse modalities within a shared embedding space. We propose Vision-Language Knowledge Graph Embeddings (VL-KGE), a framework that integrates cross-modal alignment from VLMs with structured relational modeling to learn unified multimodal representations of knowledge graphs. Experiments on WN9-IMG and two novel fine art MKGs, WikiArt-MKG-v1 and WikiArt-MKG-v2, demonstrate that VL-KGE consistently improves over traditional unimodal and multimodal KGE methods in link prediction tasks. Our results highlight the value of VLMs for multimodal KGE, enabling more robust and structured reasoning over large-scale heterogeneous knowledge graphs.

Executive Summary

This article proposes VL-KGE, a novel framework that integrates Vision-Language Models (VLMs) with structured relational modeling to learn unified multimodal representations of knowledge graphs. VL-KGE addresses the limitations of traditional knowledge graph embedding (KGE) methods by leveraging VLMs for cross-modal alignment. Experimental results demonstrate that VL-KGE consistently outperforms traditional unimodal and multimodal KGE methods in link prediction tasks. The authors' work highlights the potential of VLMs for multimodal KGE, enabling more robust and structured reasoning over large-scale heterogeneous knowledge graphs. The findings of this study have significant implications for various applications, including natural language processing, computer vision, and knowledge graph-based systems.

Key Points

  • ▸ VL-KGE integrates VLMs with structured relational modeling for multimodal KGE.
  • ▸ VL-KGE addresses limitations of traditional KGE methods by leveraging VLMs for cross-modal alignment.
  • ▸ Experimental results show VL-KGE consistently outperforms traditional KGE methods in link prediction tasks.

Merits

Strength in Multimodal Settings

VL-KGE effectively addresses the limitations of traditional KGE methods, enabling robust and structured reasoning in multimodal settings.

Improved Cross-Modal Alignment

VL-KGE leverages VLMs for cross-modal alignment, resulting in improved performance compared to traditional KGE methods.

Potential for Large-Scale Heterogeneous Knowledge Graphs

VL-KGE enables more robust and structured reasoning over large-scale heterogeneous knowledge graphs, making it a valuable tool for various applications.

Demerits

Potential Over-Reliance on VLMs

VL-KGE's reliance on VLMs may limit its applicability in scenarios where VLMs are not available or are insufficiently trained.

Lack of Evaluation in Other Tasks

The authors' experimental results focus primarily on link prediction tasks, and it would be beneficial to evaluate VL-KGE's performance on other tasks, such as entity recognition and relation extraction.

Scalability and Computational Requirements

VL-KGE's computational requirements and scalability may be a concern for large-scale knowledge graphs, and further research is needed to optimize its performance in such scenarios.

Expert Commentary

VL-KGE is a significant contribution to the field of multimodal KGE, and its results have far-reaching implications for various applications. However, further research is needed to address the limitations and challenges associated with VL-KGE. Specifically, the potential over-reliance on VLMs and the lack of evaluation in other tasks are areas that require attention. Additionally, the scalability and computational requirements of VL-KGE need to be optimized for large-scale knowledge graphs. Nevertheless, the work presented in this article represents a promising direction for multimodal KGE, and its potential to improve the performance of various applications is substantial.

Recommendations

  • ✓ Further research is needed to address the limitations and challenges associated with VL-KGE, including the potential over-reliance on VLMs and the lack of evaluation in other tasks.
  • ✓ The development of more robust and structured reasoning methods for large-scale heterogeneous knowledge graphs is essential for various applications that rely on knowledge graphs.

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