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Count-distinct SPARQL queries compute the number of unique values in the results of a query executed on a Knowledge Graph. However, counting the exact number of distinct values is often computationally demanding and time-consuming. As a result, these queries often fail on public SPARQL endpoints due to fair use policies. In this paper, we propose CRAWD, a new sampling-based approach designed to approximate count-distinct SPARQL queries. CRAWD significantly improves sampling efficiency and allows feasible execution of count-distinct SPARQL queries on public SPARQL endpoints, considerably improving existing methods.

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CRAWD: Sampling-Based Estimation of Count-Distinct SPARQL Queries

  • 会议论文

  • 发表日期：

    2025年

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  • Thi Hoang Thi Pham,
  • Pascal Molli,
  • Brice Nédelec,
  • Hala Skaf-Molli,
  • Julien Aimonier-Davat

摘要

Count-distinct SPARQL queries compute the number of unique values in the results of a query executed on a Knowledge Graph. However, counting the exact number of distinct values is often computationally demanding and time-consuming. As a result, these queries often fail on public SPARQL endpoints due to fair use policies. In this paper, we propose CRAWD, a new sampling-based approach designed to approximate count-distinct SPARQL queries. CRAWD significantly improves sampling efficiency and allows feasible execution of count-distinct SPARQL queries on public SPARQL endpoints, considerably improving existing methods.