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arXiv AI By Jiaqi Wang, Yisen Gao, Huayang Shi, Junhao Hou, Wengen Li, Jihong Guan, Yichao Zhang, Shuigeng Zhou, Yangqiu Song, Hong Cheng

From Local Recovery to Unified Generation: Discovering Triple Set in Knowledge Graphs via Observation-Anchored Latent Diffusion

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The paper introduces LatentDiff, a unified latent generative framework for triple set prediction in knowledge graphs. It replaces subgraph-based completion with a latent space graph generation approach, using a relation-aware residual-quantized autoencoder to encode the entire graph and an observation-anchored latent diffusion model to progressively denoise and recover missing triples. Experiments on three benchmark datasets demonstrate that LatentDiff surpasses state‑of‑the‑art methods by an average of 5.4 percentage points in F_{tsp}.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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