SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration

2026-08-25

AI Summary

为解决新实体在知识图谱中无连接性问题，提出SNAP-KG框架，通过投影将新实体映射到嵌入空间，实现快速聚类和高效推理。

Abstract

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.

Problem

• Knowledge Graph
• Entity Integration
• Streaming Nodes
• Graph Clustering
• Inductive Inference

Innovation

• Streaming Node Assignment
• Projection
• Inductive Inference
• Multi-view Clustering
• Knowledge Graph