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arXiv Machine Learning By Jui-Chien Lin, Mohammad Mohammadi Amiri, Oshani Seneviratne

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

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SNAP-KG is a framework for integrating newly arriving entities into knowledge graphs by assigning them to semantic communities using a projector that maps raw feature vectors into a learned embedding space. Unlike traditional multi-view graph clustering methods, SNAP-KG supports inductive inference for streaming entities without requiring graph access or model retraining. Experiments on five benchmark datasets and a large-scale KG show significant inference speedups and competitive clustering quality, while reducing candidate search for entity resolution and link prediction by up to 97%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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