RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
📰 ArXiv cs.AI
Learn how RADD improves multi-modal knowledge graph completion by decoupling retrieval and reranking, and apply this framework to your own knowledge graph projects
Action Steps
- Apply the RADD framework to your knowledge graph completion task by decoupling retrieval and reranking
- Use a relation-aware diffusion process to model complex relationships between entities
- Implement a discrete diffusion mechanism to efficiently search over the entity set
- Evaluate the performance of RADD on your dataset using metrics such as recall and precision
- Fine-tune the RADD model by adjusting hyperparameters and experimenting with different architectures
Who Needs to Know This
Data scientists and AI engineers working on knowledge graph completion tasks can benefit from this framework to improve the accuracy of their models
Key Insight
💡 Decoupling retrieval and reranking in knowledge graph completion can lead to significant improvements in accuracy and efficiency
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🚀 Improve knowledge graph completion with RADD! 🤖 Decouple retrieval and reranking for better results 📈
Key Takeaways
Learn how RADD improves multi-modal knowledge graph completion by decoupling retrieval and reranking, and apply this framework to your own knowledge graph projects
Full Article
Title: RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
Abstract:
arXiv:2604.25693v1 Announce Type: new Abstract: Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require different inductive biases. Therefore, we propose a Retrieval-Augmented Discrete Diffusion (RADD) framework to decouple retrieve and reranking for MMKGC. A relation-aware mu
Abstract:
arXiv:2604.25693v1 Announce Type: new Abstract: Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to do both retrieval over the full entity set and final decision making. We argue that this coupling is a core bottleneck: global high-recall search and local fine-grained disambiguation require different inductive biases. Therefore, we propose a Retrieval-Augmented Discrete Diffusion (RADD) framework to decouple retrieve and reranking for MMKGC. A relation-aware mu
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