DiffTSP: A Conditional Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
Triple set prediction (TSP) aims to discover missing facts from an incomplete knowledge graph without predefined queries. Existing approaches typically construct the predicted set through triple-level scoring, sometimes preceded by entity pair filtering that can exclude valid missing facts. We propose DiffTSP, a conditional discrete diffusion model that formulates missing fact discovery as conditional subgraph generation. In each subgraph, DiffTSP directly predicts the missing relational structure without a separate entity pair selection stage. The model learns to generate masked target relation states at different corruption levels, with the observed support subgraph retained as conditio...
| Authors | Jihong Guan, Jiaqi Wang, Han Cai et al. |
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| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 14 October 2026 |
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