SNAP-KG：用于知识图谱实体集成的基于投影的流式节点分配
SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration
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中文总结 AI 辅助
该研究提出SNAP-KG框架，通过训练投影器实现流式知识图谱实体的快速分配，在多个基准数据集和生产级KG上实现推理速度大幅提升与候选搜索范围显著缩小。
中文摘要 AI 辅助
知识图谱（KG）构建流程必须持续将新到达的实体整合到不断增长的图谱中。与在现有节点之间插入三元组不同，新到达的实体没有图连通性：它在获取阶段以原始特征向量的形式出现，必须被分配到一个语义社区，才能在可处理的候选集上进行实体解析和链接预测。现有的多视图图聚类方法利用多种关系类型作为结构视图，但属于转导式方法：它们假设图是固定的，无法在不重新训练的情况下分配未见实体。我们提出SNAP-KG（Streaming Node Assignment via Projection for Knowledge Graph Entity Integration），这是一个支持图结构多视图关系聚类和流式实体归纳推理的框架。SNAP-KG训练一个投影器，仅使用原始特征将新实体直接映射到学习到的嵌入空间，从而无需图访问或模型重新训练即可立即进行聚类分配。在五个基准多视图图数据集和一个拥有240万个节点的生产级KG上进行的实验表明，与基于重新训练的方法相比，SNAP-KG实现了多个数量级的推理速度提升，且聚类质量具有竞争力。作为下游任务的候选范围界定机制，SNAP-KG在五个基准数据集上实现了62%-75%的候选搜索减少，在OGB-WikiKG2数据集上实现了97%的候选搜索减少，适用于实体解析和链接预测任务。
英文摘要
Knowledge graph (KG) construction pipelines must continuously integrate newly arriving entities into a growing graph. Unlike inserting triples between existing nodes, a newly arriving entity has no graph connectivity: it emerges from the acquisition phase as a raw feature vector and must be assigned to a semantic community before entity resolution and link prediction can operate over a tractable candidate set. Existing multi-view graph clustering methods exploit multiple relation types as structural views, but are transductive: they assume a fixed graph and cannot assign unseen entities without retraining. We propose SNAP-KG (Streaming Node Assignment via Projection for Knowledge Graph Entity Integration), a framework supporting graph-structural multi-view relational clustering and inductive inference for streaming entities. SNAP-KG trains a projector to map a new entity directly to the learned embedding space using only raw features, enabling immediate cluster assignment without graph access or model retraining. Experiments on five benchmark multi-view graph datasets and a production-scale KG of 2.4 million nodes demonstrate multiple orders-of-magnitude inference speedups over retraining-based approaches and competitive clustering quality. As a candidate scoping mechanism for downstream tasks, SNAP-KG achieves 62-75% candidate search reduction on the five benchmark datasets and 97% on OGB-WikiKG2 for entity resolution and link prediction.
发表机构
- Rensselaer Polytechnic Institute（伦斯勒理工学院）
机构由 AI 辅助整理，请以论文原文为准。
