Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expensive and vary in complexity, as each involves a different number of target nodes linked to subgraphs of diverse densities and structures. Existing acceleration methods, such as pruning, quantization, and knowledge distillation, instantiate smaller models but do not adapt them to the structure or semantics of individual queries. They also store models as monolithic files that must be fully loaded, and miss the opportunity to retrieve only the neighboring nodes and corresponding model components that are semantically relevant to the target nodes. These limitations lead to excessive data loading and redundant computation on large KGs. This paper presents KG-WISE, a task-driven inference paradigm for large KGs. KG-WISE decomposes trained GNN models into fine-grained components that can be partially loaded based on the structure of the queried subgraph. It employs large language models (LLMs) to generate reusable query templates that extract semantically relevant subgraphs for each task, enabling query-aware and compact model instantiation. We evaluate KG-WISE on six large KGs with up to 42 million nodes and 166 million edges. KG-WISE achieves up to 28x faster inference and 98% lower memory usage than state-of-the-art systems while maintaining or improving accuracy across both commercial and open-weight LLMs.

翻译：在大规模知识图谱（KGs）上实现高效的图神经网络（GNN）推理对于众多实际应用至关重要。GNN推理查询计算成本高昂且复杂度各异，因为每个查询涉及不同数量的目标节点，这些节点与密度和结构各异的子图相关联。现有的加速方法（如剪枝、量化和知识蒸馏）虽然能实例化较小的模型，但未能使其适应个体查询的结构或语义。这些方法还将模型存储为必须完整加载的单一文件，错失了仅检索与目标节点语义相关的相邻节点及对应模型组件的机会。这些局限导致在大规模知识图谱上存在过度的数据加载和冗余计算。本文提出KG-WISE，一种面向大规模知识图谱的任务驱动推理范式。KG-WISE将训练好的GNN模型分解为细粒度组件，可根据查询子图的结构进行部分加载。它利用大语言模型（LLMs）生成可复用的查询模板，为每个任务提取语义相关的子图，从而实现查询感知的紧凑模型实例化。我们在六个大规模知识图谱（最大规模达4200万个节点和1.66亿条边）上评估KG-WISE。实验表明，相较于前沿系统，KG-WISE在保持或提升精度的同时，实现了高达28倍的推理加速和98%的内存使用降低，该结果在商业和开源权重的LLMs上均得到验证。