AI 中文总结
该研究针对多模态少样本知识图谱补全难题，提出双路径大语言模型推理框架DuPLeR，结合多模态LLM先验与事实支撑构建校准关系图，经实验验证其在数据稀缺场景下性能稳健。
AI 中文摘要
知识图谱补全（KGC）旨在推断知识图谱（KG）中缺失的事实，从而提升其完整性并支撑下游智能应用。然而，实际部署中出现的新实体和关系使得归纳式KGC变得困难，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）衍生的先验知识可丰富稀疏的关系上下文，但也可能引入噪声或幻觉证据。为解决这些问题，我们提出DuPLeR，一个用于多模态少样本KGC的双路径大语言模型推理框架。DuPLeR通过结合多模态LLM衍生的类型先验与事实支撑结构构建校准的关系图，并对优化后的关系拓扑执行双层级结构推理。此外，双路径多模态增强模块利用与查询相关的多模态信号调控消息传递，并在图传播后补充实体表示。在两个多模态KG（MMKG）基准的8个归纳式变体上的实验表明，DuPLeR在数据稀缺的KGC场景中实现了稳健的性能。
英文摘要
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.
Comments10 pages, 4 figures
