Papers by Xiangfeng Luo
HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction (2025.findings-emnlp)
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| Challenge: | Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise. |
| Approach: | They propose a conditional denoising diffusion framework that learns to reverse structured noise corruption. |
| Outcome: | The proposed framework outperforms state-of-the-art HKGC methods in a variety of noisy conditions. |
COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion (2024.naacl-long)
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| Challenge: | Existing methods for knowledge graph completion (KGC) are limited in generality and scalability due to poor contextual facts. |
| Approach: | They propose a contextual facts collector and contextual facts organizer to enhance the inference ability of GM-based methods for various KGC tasks. |
| Outcome: | The proposed model outperforms state-of-the-art methods in terms of performance. |