ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion

2025-05-12

AI Summary

To address the degradation of few-shot link prediction performance caused by long-tail relations in knowledge graphs, this paper proposes Relation-Conditioned Diffusion with Attention Pooling (ReCDAP). ReCDAP introduces negative triples as structured signals into diffusion modeling for the first time, establishing a dual-path latent distribution framework that separately models positive and negative relations. It further employs a relation-aware attention pooling mechanism to explicitly capture discriminative differences between them. Integrated with few-shot meta-learning and negative sampling augmentation, ReCDAP jointly models both semantic and discriminative information of triples under sparse relations. Extensive experiments on FB15k-237 and NELL-995 demonstrate that ReCDAP significantly improves link prediction accuracy in few-shot settings, achieving state-of-the-art (SOTA) performance.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Knowledge Graphs (KGs), composed of triples in the form of (head, relation, tail) and consisting of entities and relations, play a key role in information retrieval systems such as question answering, entity search, and recommendation. In real-world KGs, although many entities exist, the relations exhibit a long-tail distribution, which can hinder information retrieval performance. Previous few-shot knowledge graph completion studies focused exclusively on the positive triple information that exists in the graph or, when negative triples were incorporated, used them merely as a signal to indicate incorrect triples. To overcome this limitation, we propose Relation-Based Conditional Diffusion with Attention Pooling (ReCDAP). First, negative triples are generated by randomly replacing the tail entity in the support set. By conditionally incorporating positive information in the KG and non-existent negative information into the diffusion process, the model separately estimates the latent distributions for positive and negative relations. Moreover, including an attention pooler enables the model to leverage the differences between positive and negative cases explicitly. Experiments on two widely used datasets demonstrate that our method outperforms existing approaches, achieving state-of-the-art performance. The code is available at https://github.com/hou27/ReCDAP-FKGC.

Problem

• Addresses few-shot knowledge graph completion challenges
• Utilizes positive and negative triples in diffusion process
• Improves performance with attention pooling mechanism

Innovation

• Generates negative triples via tail replacement
• Uses conditional diffusion for relation estimation
• Incorporates attention pooling for case differences