COREKG uses sensitivity-based importance sampling from coreset theory to generate personalized KG summaries that achieve higher query accuracy and structural coverage than prior methods while using only a tiny fraction of the original graph.
Scalable k-means clustering via lightweight core- sets
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COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs
COREKG uses sensitivity-based importance sampling from coreset theory to generate personalized KG summaries that achieve higher query accuracy and structural coverage than prior methods while using only a tiny fraction of the original graph.