lubm4obda
Inference and Meta Knowledge Benchmarking of OBDA Systems
Science Score: 67.0%
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✓CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 8 DOI reference(s) in README -
✓Academic publication links
Links to: acm.org, zenodo.org -
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.4%) to scientific vocabulary
Keywords
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Repository
Inference and Meta Knowledge Benchmarking of OBDA Systems
Basic Info
- Host: GitHub
- Owner: oeg-upm
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://doi.org/10.5281/zenodo.7110221
- Size: 6.47 MB
Statistics
- Stars: 4
- Watchers: 6
- Forks: 0
- Open Issues: 0
- Releases: 1
Topics
Metadata Files
README.md
The LUBM4OBDA Benchmark is an extension of the popular LUBM Benchmark to evaluate Ontology-Based Data Access (OBDA) engines over relational databases. In addition, LUBM4OBDA considers meta knowledge (also called reification or statement-level metadata) benchmarking. The main characteristics of LUBM4OBDA are:
- SQL data dumps for MySQL and PostgreSQL.
- Data generator for custom scaling factors.
- Original LUBM query set (queries 1-14).
- Meta knowledge query set for standard reification, singleton property and SPARQL-star (queries 15-18).
- R2RML and RML mappings.
Citing LUBM4OBDA: please cite the JWE paper:
bib
@article{arenas2024lubm4obda,
title = {{LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge}},
author = {Arenas-Guerrero, Julián and Pérez, María S. and Corcho, Oscar},
journal = {Journal of Web Engineering},
publisher = {River Publishers},
issn = {1544-5976},
year = {2024},
volume = {22},
number = {8},
pages = {1163–1186},
doi = {10.13052/jwe1540-9589.2284}
}
Data
There are two options to obtain the SQL data dumps:
- Download the SQL data dumps for scaling factors 1, 10, 100 and 1000 from Zenodo.
- Use the Docker container with the data generator to produce the data with custom scaling factors.
Mappings
The mappings directory of this GitHub repository contains all the R2RML and RML documents. The following mappings are provided:
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- Original, without meta knowledge.
- Standard reification.
- Singleton property.
- R2RML-star.
RML:
- Original, without meta knowledge.
- Standard reification.
- Singleton property.
- RML-star.
Ontology
The Univ-Bench ontology is available in the ontology directory of this GitHub repository.
Queries
The queries are available in the queries directory of this GitHub repository. Keep in mind that original mappings should be used for queries 1-14. There are three different versions of queries 15-18, one for each meta knowledge approach (standard reification, singleton property or RDF-star), with each approach having its corresponding mapping.
CSV & Apache Parquet
It is also possible to run the benchmark with CSV and Apache Parquet files. The resources for these data sources are available in Zenodo and they have been described in an ESWC paper.
Owner
- Name: Ontology Engineering Group (UPM)
- Login: oeg-upm
- Kind: organization
- Email: oeg-dev@delicias.dia.fi.upm.es
- Location: Boadilla del Monte, Madrid, Spain
- Website: https://oeg.fi.upm.es/
- Repositories: 294
- Profile: https://github.com/oeg-upm
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
authors:
- family-names: Arenas-Guerrero
given-names: Julián
- family-names: Pérez
given-names: María S.
- family-names: Corcho
given-names: Oscar
title: "LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge"
type: article
journal: Journal of Web Engineering
doi: 10.13052/jwe1540-9589.2284
year: 2024
volume: 22
issue: 8
GitHub Events
Total
Last Year
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Julián Arenas-Guerrero | j****g@h****m | 50 |
| Julián Arenas Guerrero | a****n@o****m | 23 |
| Julián Arenas Guerrero | 1****n | 19 |
| Daniel Doña | d****a@x****a | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 2 years ago
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- Total pull requests: 0
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- Average comments per issue: 0
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Past Year
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- Average comments per issue: 0
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- Bot issues: 0
- Bot pull requests: 0