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2025 | OriginalPaper | Chapter

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

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

Published in: The Semantic Web – ISWC 2024

Publisher: Springer Nature Switzerland

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Abstract

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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previous chapter DUNKS: Chunking and Summarizing Large and Heterogeneous Data for Dataset Search
next chapter Exploiting Distant Supervision to Learn Semantic Descriptions of Tables with Overlapping Data
Footnotes
1
Both engines crash after 5 h, running out of memory.
 
3
We were unable to compute frequencies of distinct subjects and objects for WDBench with our computing resources.
 
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Metadata
Title
CRAWD: Sampling-Based Estimation of Count-Distinct SPARQL Queries
Authors
Thi Hoang Thi Pham
Pascal Molli
Brice Nédelec
Hala Skaf-Molli
Julien Aimonier-Davat
Copyright Year
2025
Book
The Semantic Web – ISWC 2024

Print ISBN: 978-3-031-77849-0

Electronic ISBN: 978-3-031-77850-6

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-77850-6_6

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