用双路径推理提升少样本多模态知识图谱补全效果
Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
- 双路径设计分别处理类型先验与事实支持，增强关系图可靠性
- 在8个数据稀缺场景中表现稳定，显著优于基线方法
- 适合需要少样本推理的多模态知识系统开发者
知识图谱补全（KGC）旨在推断知识图谱中的缺失事实，从而提升其完整性并支持下游智能应用。然而，现实部署中不断出现的新实体和关系使得归纳式KGC面临挑战，尤其在少样本和零样本设置下。多模态信息与大语言模型（LLM）提供的先验可丰富稀疏的关系上下文，但也可能引入噪声或幻觉证据。为此，我们提出DuPLeR——一种用于多模态少样本KGC的双路径LLM推理框架。DuPLeR通过融合多模态LLM推导的类型先验与真实支持结构，构建校准的关系图，并在此基础上进行双层结构推理。此外，一个双路径多模态增强模块利用查询相关的多模态信号调节消息传递，并在图传播后补充实体表示。在两个多模态知识图谱（MMKG）基准的八个归纳变体上进行实验，结果表明DuPLeR在数据稀缺的KGC场景中表现出稳健性能。
原文摘要 · Abstract (English)
Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. However, emerging entities and relations in real-world deployments make inductive KGC difficult, especially under few-shot and zero-shot settings. Multimodal information and Large Language Model (LLM)-derived priors can enrich sparse relational contexts, but they may also introduce noisy or hallucinated evidence. To address these issues, we propose DuPLeR, a \textbf{Du}al-\textbf{P}ath \textbf{L}LM \textbf{R}easoning framework for multimodal few-shot KGC. DuPLeR builds a calibrated relation graph by combining multimodal LLM-derived type priors with factual support structures, and performs dual-level structural reasoning over the refined relation topology. Moreover, a dual-pathway multimodal enhancement module regulates message passing with query-relevant multimodal signals and supplements entity representations after graph propagation. Experiments on eight inductive variants of two multimodal KG (MMKG) benchmarks show that DuPLeR achieves robust performance in data-scarce KGC scenarios.
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