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TA-KAND: Two-stage Attention Triple Enhancement and U-KAN based Diffusion For Few-shot Knowledge Graph Completion

arXiv 2512.12182 ↗First posted 13 Dec 2025cs.AI cs.LGcited by 0

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Abstract (arXiv)

Knowledge Graphs have become fundamental infrastructure for applications such as intelligent question answering and recommender systems due to their expressive representation. Nevertheless, real-world knowledge is heterogeneous, leading to a pronounced long-tailed distribution over relations. Previous studies mainly based on metric matching or meta learning. However, they often overlook the distributional characteristics of positive and negative triple samples. In this paper, we propose a few-shot knowledge graph completion framework that integrates two-stage attention triple enhancer with U-KAN based diffusion model. Extensive experiments on two public datasets show significant advantages of our methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 13 Dec 2025.

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13 of 17 references.

References · 13

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1 more
  1. 2023Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completioncited by 19

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  1. 2026One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
  2. 2025DANS-KGC: Diffusion Based Adaptive Negative Sampling for Knowledge Graph Completion
  3. 2026RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
  4. 2023Collective Knowledge Graph Completion with Mutual Knowledge Distillation
  5. 2025DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completion
  6. 2024Graphusion: A RAG Framework for Knowledge Graph Construction with a Global Perspective
  7. 2026MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts for Multimodal Knowledge Graph Completion
  8. 2024GS-KGC: A Generative Subgraph-based Framework for Knowledge Graph Completion with Large Language Models