Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens
arXiv:2602.15896v1 Announce Type: new Abstract: Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works are designed for a transductive setting, which learns dataset-specific embeddings and struggles to generalize to new KGs. Recent knowledge graph foundation models (KGFMs) improve cross-KG transfer, but they mainly exploit structural patterns and ignore rich multi-modal signals. We address these gaps by proposing a token-based foundation model (TOFU) for MMKGR, which exhibits strong generalization across different MMKGs. TOFU discretizes structural, visual, and textual information into modality-specific tokens. TOFU then employs a hierarchical fusion architecture with mixture-of-message mechanisms, aiming to process these tokens and obtain transferable features for MMKGR. Experimental results on 17 transductive, inductive, and fully-inductive MMKGs show that TOF
arXiv:2602.15896v1 Announce Type: new Abstract: Multi-modal knowledge graph reasoning (MMKGR) aims to predict the missing links by exploiting both graph structure information and multi-modal entity contents. Most existing works are designed for a transductive setting, which learns dataset-specific embeddings and struggles to generalize to new KGs. Recent knowledge graph foundation models (KGFMs) improve cross-KG transfer, but they mainly exploit structural patterns and ignore rich multi-modal signals. We address these gaps by proposing a token-based foundation model (TOFU) for MMKGR, which exhibits strong generalization across different MMKGs. TOFU discretizes structural, visual, and textual information into modality-specific tokens. TOFU then employs a hierarchical fusion architecture with mixture-of-message mechanisms, aiming to process these tokens and obtain transferable features for MMKGR. Experimental results on 17 transductive, inductive, and fully-inductive MMKGs show that TOFU consistently outperforms strong KGFM and MMKGR baselines, delivering strong performance on unseen MMKGs.
Executive Summary
The article 'Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokens' introduces a novel token-based foundation model (TOFU) for multi-modal knowledge graph reasoning (MMKGR). TOFU addresses the limitations of existing models by discretizing structural, visual, and textual information into modality-specific tokens and employing a hierarchical fusion architecture. The model demonstrates strong generalization across different MMKGs, outperforming existing knowledge graph foundation models (KGFMs) and MMKGR baselines in various settings. The study highlights the importance of leveraging multi-modal signals for improved cross-KG transfer and reasoning.
Key Points
- ▸ TOFU discretizes structural, visual, and textual information into modality-specific tokens.
- ▸ TOFU employs a hierarchical fusion architecture with mixture-of-message mechanisms.
- ▸ TOFU consistently outperforms strong KGFM and MMKGR baselines across 17 MMKGs.
Merits
Innovative Approach
The use of fine-grained, transferable multi-modal tokens is a novel approach that effectively captures and processes rich multi-modal signals, enhancing the model's generalization capabilities.
Strong Generalization
TOFU demonstrates strong performance on unseen MMKGs, addressing the limitations of existing models that struggle with generalization.
Comprehensive Evaluation
The study provides a thorough evaluation across 17 MMKGs, including transductive, inductive, and fully-inductive settings, showcasing the model's versatility and robustness.
Demerits
Complexity
The hierarchical fusion architecture and mixture-of-message mechanisms may introduce complexity in implementation and computational requirements.
Data Dependency
The effectiveness of TOFU relies heavily on the availability and quality of multi-modal data, which may not always be readily accessible or uniformly distributed.
Scalability
The scalability of TOFU to extremely large and diverse knowledge graphs remains to be thoroughly investigated, as the current study focuses on a specific set of MMKGs.
Expert Commentary
The article presents a significant advancement in the field of multi-modal knowledge graph reasoning by introducing TOFU, a token-based foundation model that effectively captures and processes rich multi-modal signals. The model's strong generalization capabilities across different MMKGs address a critical limitation of existing approaches, which often struggle with dataset-specific embeddings and generalization to new knowledge graphs. The hierarchical fusion architecture and mixture-of-message mechanisms demonstrate a sophisticated approach to integrating structural, visual, and textual information, resulting in transferable features that enhance reasoning performance. The comprehensive evaluation across 17 MMKGs, including transductive, inductive, and fully-inductive settings, provides robust evidence of TOFU's versatility and robustness. However, the complexity of the model and its dependency on high-quality multi-modal data present challenges that need to be addressed for broader adoption. The scalability of TOFU to extremely large and diverse knowledge graphs remains an open question, warranting further investigation. Overall, the study contributes valuable insights to the fields of multi-modal learning, knowledge graph embeddings, and foundation models, paving the way for more advanced and generalized AI systems.
Recommendations
- ✓ Further research should explore the scalability of TOFU to extremely large and diverse knowledge graphs to assess its performance in real-world, large-scale applications.
- ✓ Investigating the robustness of TOFU in handling noisy or incomplete multi-modal data would provide valuable insights into its practical applicability in real-world scenarios.
