Snapquery EKAW 2024 paper submission: Difference between revisions

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== Key Points ==
== Introduction ==
# Query rot versus link rot
# ★★★★★ Query rot versus link rot
# Transparency vs. complexity of SPARQL queries
# ★★★★☆ Transparency vs. complexity of SPARQL queries
# Use cases for named queries
# ★★★★☆ Use cases for named queries
# Ambiguity of names
# ★★★★☆ Persistent identifiers
# Persistent identifiers
# ★★★☆☆ Query hashes and short_urls
## Query hashes and short_urls
== Mitigation Query Rot using snapquery ==
# How to deal with aspects that do not (usually) influence the execution of a SPARQL query, like whitespace, comments, capitalization and variable names?
# ★★★★★ Parameterized queries
# Parameterized queries
# ★★★☆☆ https://web.archive.org/web/20150512231123/http://answers.semanticweb.com:80/questions/12147/whats-the-best-way-to-parameterize-sparql-queries
## https://web.archive.org/web/20150512231123/http://answers.semanticweb.com:80/questions/12147/whats-the-best-way-to-parameterize-sparql-queries
# ★★★☆☆ https://jena.apache.org/documentation/query/parameterized-sparql-strings.html
## https://jena.apache.org/documentation/query/parameterized-sparql-strings.html
# ★★★★☆ Scholia Jinja templates
## Scholia Jinja templates
# ★★★★☆ Technical debt and accidental complexity
# technical debt and accidential complexity
# ★★★☆☆ How to deal with aspects that do not (usually) influence the execution of a SPARQL query, like whitespace, comments, capitalization and variable names?
# Wikidata example queries
== SnapQuery Implementation ==
# Scholia and Wikidata graph split
# ★★★★☆ SPARQL standard changes
# Other knowledge graphs, e.g., DBLP, OpenStreetMap
# ★★★☆☆ Natural Language input
# Perhaps also some NFDI examples or some custom knowledge graphs like FAIRJupyter
# ★★★☆☆ Automatic syntax repairs
# An Ngo Lam's ESWC 2023 paper as a pointer to the style of comparison
# ★★★☆☆ Automatic conversion of SQL input, SPARQL output
# Quality criteria https://github.com/WolfgangFahl/snapquery/issues/26
== Evaluation ==
# List of standard refactoring activities and the support by this approach
#★★★★☆ Wikidata example queries
# SPARQL standard changes
#★★★★★ Scholia and Wikidata graph split
# Getting your own copy of Wikidata; the infrastructure effort needs to be mentioned
#★★★☆☆ Other knowledge graphs, e.g., DBLP, OpenStreetMap
# W3C test set - why did we not use that as an example
#★★☆☆☆ Perhaps also some NFDI examples or some custom knowledge graphs like FAIRJupyter
# Useability evaluation https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/
#★★★★★ Quality criteria https://github.com/WolfgangFahl/snapquery/issues/26
# https://github.com/ad-freiburg/qlever/wiki/QLever-performance-evaluation-and-comparison-to-other-SPARQL-engines
#★★★★☆ List of standard refactoring activities and the support by this approach
# Natural Language input
#★★★★☆ Getting your own copy of Wikidata; the infrastructure effort needs to be mentioned
## Automatic syntax repairs
#★★★☆☆ Usability evaluation https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/
## automatic conversion of SQL input, SPARQL output.
#★★★★☆ https://github.com/ad-freiburg/qlever/wiki/QLever-performance-evaluation-and-comparison-to-other-SPARQL-engines
# A closed issue should have at least one example that runs
#★★★★☆ A closed issue should have at least one example that runs
# Hypothesis by Stefan Decker: Query rot is more prominent in KG environments than with relational databases.
== Conclusion and Future Work ==
# https://stackoverflow.com/questions/tagged/sparql
#★★★★★ Hypothesis by Stefan Decker: Query rot is more prominent in KG environments than with relational databases
# https://www.semantic-web-journal.net/system/files/swj3076.pdf
#★★★☆☆ Ambiguity of names
# https://arxiv.org/pdf/cs/0605124
== Additional Resources ==
# https://arxiv.org/pdf/1402.0576 optimizing queries
#★★☆☆☆ https://stackoverflow.com/questions/tagged/sparql
# https://www.w3.org/TR/REC-rdf-syntax/
#★★★☆☆ https://www.semantic-web-journal.net/system/files/swj3076.pdf
# https://biblio.ugent.be/publication/8632551/file/8653456 Towards supporting multiple semantics of named graphs using N3 rules
#★★☆☆☆ https://arxiv.org/pdf/cs/0605124
# ESWC 2019 proceedings (978-3-030-21348-0.pdf)
#★★★☆☆ https://arxiv.org/pdf/1402.0576 optimizing queries
# Linked Data Fragments https://linkeddatafragments.org/  e.g. https://ldfclient.wmflabs.org/ 404 error
#★★☆☆☆ https://www.w3.org/TR/REC-rdf-syntax/
#★★☆☆☆ https://biblio.ugent.be/publication/8632551/file/8653456 Towards supporting multiple semantics of named graphs using N3 rules
#★★★☆☆ ESWC 2019 proceedings (978-3-030-21348-0.pdf)
#★★☆☆☆ Linked Data Fragments https://linkeddatafragments.org/  e.g. https://ldfclient.wmflabs.org/ 404 error


== Related Work ==
== Related Work ==
★★★☆☆ Link rot
★★★★☆ Information Hiding and Dependency Inversion Principles
★★★☆☆ Federated Queries
★★★☆☆ grlc
★★☆☆☆ querypulator
=== Testsuites ===
=== Testsuites ===
==== W3C SPARQL 1.1 Test Suite ====
==== ★★★☆☆ 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
* Official test suite developed by the W3C SPARQL Working Group
Line 49: Line 59:


=== Benchmarks ===
=== Benchmarks ===
==== An Ngoc Lam et al.'s ESWC 2023 paper "Evaluation of a Representative Selection of SPARQL Query Engines Using Wikidata" ====
==== ★★★★☆ 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
* Evaluates performance of 5 RDF triplestores and 1 experimental SPARQL engine
* Uses complete version of Wikidata knowledge graph
* Uses complete version of Wikidata knowledge graph

Revision as of 13:29, 9 July 2024

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

★★★☆☆ Link rot ★★★★☆ Information Hiding and Dependency Inversion Principles ★★★☆☆ Federated Queries ★★★☆☆ grlc ★★☆☆☆ 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
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