Snapquery EKAW 2024 paper

Introduction

1. ★★★★★ Query rot versus link rot
2. ★★★★☆ Transparency vs. complexity of SPARQL queries
3. ★★★★☆ Use cases for named queries
4. ★★★★☆ Persistent identifiers
5. ★★★☆☆ Query hashes and short_urls

Mitigation Query Rot using snapquery

1. ★★★★★ Parameterized queries
2. ★★★☆☆ https://web.archive.org/web/20150512231123/http://answers.semanticweb.com:80/questions/12147/whats-the-best-way-to-parameterize-sparql-queries
3. ★★★☆☆ https://jena.apache.org/documentation/query/parameterized-sparql-strings.html
4. ★★★★☆ Scholia Jinja templates
5. ★★★★☆ Technical debt and accidental complexity
6. ★★★☆☆ How to deal with aspects that do not (usually) influence the execution of a SPARQL query, like whitespace, comments, capitalization and variable names?

SnapQuery Implementation

1. ★★★★☆ SPARQL standard changes
2. ★★★☆☆ Natural Language input
3. ★★★☆☆ Automatic syntax repairs
4. ★★★☆☆ Automatic conversion of SQL input, SPARQL output

Evaluation

1. ★★★★☆ Wikidata example queries
2. ★★★★★ Scholia and Wikidata graph split
3. ★★★☆☆ Other knowledge graphs, e.g., DBLP, OpenStreetMap
4. ★★☆☆☆ Perhaps also some NFDI examples or some custom knowledge graphs like FAIRJupyter
5. ★★★★★ Quality criteria https://github.com/WolfgangFahl/snapquery/issues/26
6. ★★★★☆ List of standard refactoring activities and the support by this approach
7. ★★★★☆ Getting your own copy of Wikidata; the infrastructure effort needs to be mentioned
8. ★★★☆☆ Usability evaluation https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/
9. ★★★★☆ https://github.com/ad-freiburg/qlever/wiki/QLever-performance-evaluation-and-comparison-to-other-SPARQL-engines
10. ★★★★☆ A closed issue should have at least one example that runs

Conclusion and Future Work

1. ★★★★★ Hypothesis by Stefan Decker: Query rot is more prominent in KG environments than with relational databases
2. ★★★☆☆ Ambiguity of names

Additional Resources

1. ★★☆☆☆ https://stackoverflow.com/questions/tagged/sparql
2. ★★★☆☆ https://www.semantic-web-journal.net/system/files/swj3076.pdf
3. ★★☆☆☆ https://arxiv.org/pdf/cs/0605124
4. ★★★☆☆ https://arxiv.org/pdf/1402.0576 optimizing queries
5. ★★☆☆☆ https://www.w3.org/TR/REC-rdf-syntax/
6. ★★☆☆☆ https://biblio.ugent.be/publication/8632551/file/8653456 Towards supporting multiple semantics of named graphs using N3 rules
7. ★★★☆☆ ESWC 2019 proceedings (978-3-030-21348-0.pdf)
8. ★★☆☆☆ Linked Data Fragments https://linkeddatafragments.org/ e.g. https://ldfclient.wmflabs.org/ 404 error

Related Work

1. ★★★☆☆ Link rot
2. ★★★★☆ Information Hiding and Dependency Inversion Principles
3. ★★★☆☆ Federated Queries
4. ★★★☆☆ grlc
5. ★★☆☆☆ querypulator

Testsuites

★★★☆☆ W3C SPARQL 1.1 Test Suite

W3C test set - why did we not use that as an example

• Official test suite developed by the W3C SPARQL Working Group
• Designed to test conformance to the SPARQL 1.1 specification
• Covers a wide range of SPARQL features and edge cases
• Primarily focused on correctness rather than performance

see https://wikitech.wikimedia.org/wiki/User:AndreaWest/WDQS_Testing/Running_TFT

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