Snapquery EKAW 2024 paper

Key Points

1. Query rot versus link rot
2. Transparency vs. complexity of SPARQL queries
3. Use cases for named queries
4. Ambiguity of names
5. Persistent identifiers
   1. Query hashes and short_urls
6. How to deal with aspects that do not (usually) influence the execution of a SPARQL query, like whitespace, comments, capitalization and variable names?
7. Parameterized queries
   1. https://web.archive.org/web/20150512231123/http://answers.semanticweb.com:80/questions/12147/whats-the-best-way-to-parameterize-sparql-queries
   2. https://jena.apache.org/documentation/query/parameterized-sparql-strings.html
   3. Scholia Jinja templates
8. technical debt and accidential complexity
9. Wikidata example queries
10. Scholia and Wikidata graph split
11. Other knowledge graphs, e.g., DBLP, OpenStreetMap
12. Perhaps also some NFDI examples or some custom knowledge graphs like FAIRJupyter
13. An Ngo Lam's ESWC 2023 paper as a pointer to the style of comparison
14. Quality criteria https://github.com/WolfgangFahl/snapquery/issues/26
15. List of standard refactoring activities and the support by this approach
16. SPARQL standard changes
17. Getting your own copy of Wikidata; the infrastructure effort needs to be mentioned
18. W3C test set - why did we not use that as an example
19. Useability evaluation https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/
20. https://github.com/ad-freiburg/qlever/wiki/QLever-performance-evaluation-and-comparison-to-other-SPARQL-engines
21. Natural Language input
    1. Automatic syntax repairs
    2. automatic conversion of SQL input, SPARQL output.
22. A closed issue should have at least one example that runs
23. Hypothesis by Stefan Decker: Query rot is more prominent in KG environments than with relational databases.
24. https://stackoverflow.com/questions/tagged/sparql
25. https://www.semantic-web-journal.net/system/files/swj3076.pdf
26. https://arxiv.org/pdf/cs/0605124
27. https://arxiv.org/pdf/1402.0576 optimizing queries
28. https://www.w3.org/TR/REC-rdf-syntax/
29. https://biblio.ugent.be/publication/8632551/file/8653456 Towards supporting multiple semantics of named graphs using N3 rules
30. Wikidata Graph Pattern Benchmark (WGPB) for RDF/SPARQL by Aidan Hogan et al., 2020 (DOI: 10.5281/zenodo.4035223)
31. ESWC 2019 proceedings (978-3-030-21348-0.pdf)
32. Linked Data Fragments https://linkeddatafragments.org/ e.g. https://ldfclient.wmflabs.org/ 404 error

Related Work

Benchmarks

An Ngoc Lam et al.'s ESWC 2023 paper "Evaluation of a Representative Selection of SPARQL Query Engines Using Wikidata"

• Evaluates performance of 5 RDF triplestores and 1 experimental SPARQL engine
• Uses complete version of Wikidata knowledge graph
• Compares importing time, loading time, exporting time, and query performance
• Evaluates 328 queries defined by Wikidata users
• Also uses SP2Bench synthetic benchmark for comparison
• Provides detailed analysis of query execution plans and profiling information
• Offers insights on triplestore performance with large-scale real-world data

Wikidata Graph Pattern Benchmark (WGPB) for RDF/SPARQL by Aidan Hogan et al., 2020

DOI: 10.5281/zenodo.4035223

• Focuses on evaluating performance of graph pattern matching in SPARQL engines
• Uses a subset of Wikidata as the dataset
• Provides a large set of SPARQL basic graph patterns
• Designed to test the benefits of worst-case optimal join algorithms
• Exhibits a variety of increasingly complex join patterns
• Allows for systematic testing of query optimization techniques
• Offers insights into the performance characteristics of different SPARQL engines on complex graph patterns