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https://github.com/cefriel/mapping-template-eval

https://github.com/cefriel/mapping-template-eval

Science Score: 26.0%

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Basic Info
  • Host: GitHub
  • Owner: cefriel
  • Language: Python
  • Default Branch: main
  • Size: 369 KB
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Created over 3 years ago · Last pushed almost 2 years ago
Metadata Files
Readme

README.md

mapping-template Evaluation

Repository containing artifacts and results for the mapping-template evaluation.

Qualitative Evaluation

Examples defined for the evaluation are in the mapping-template repository (examples). In this repository we report the table summarising the evaluation against the Conceptual Mapping ontology requirements (repository).

Additionally the folder kgc-challenge-2024\track1 contain the configuration file, the generated templates and the obtained results for evaluating the compliance of the mapping-template tool w.r.t all the test cases of the RML Core module specification.

Quantitative Evaluation

Preliminary tests

To test the performance and scalability of the mapping-template tool, we considered the GTFS-Madrid-Bench. The benchmark provides a set of (R2)RML mappings and a generator to create input data sources in different formats and sizes. We considered three data formats (CSV, XML and JSON) and three scaling factors (1,10,100) comparing the mapping-template tool with the rmlmapper v6.1.2 and morph-kgc v2.3.1 processors. We adopted a set of RML mappings simplifying the join operation for the GTFS shapes file as in [1].

A set of templates implementing the same mapping rules were generated for the mapping-template tool (*-no-self-join.vm). In this first set of templates, we defined a join operation between two data frames as specified by the join condition in RML to guarantee a more fair comparison. An additional set of templates (*-no-join.vm), compared in the evaluation as mapping-template-nj, is defined to test the performances of the template approach using optimised mappings without join operations.

[1] Arenas-Guerrero, Julián, et al. "Knowledge graph construction with R2RML and RML: an ETL system-based overview." Second International Workshop on Knowledge Graph Construction. 2021.

Knowledge Graph Construction Challenge

Configuration files for the participation in the KGCW Challenge 2024 and subsequent tests for the SWJ paper are contained in the kgc-challenge-2024 folder. The result of other engines are taken from the challenge results published by the organisers at https://doi.org/10.5281/zenodo.11577087.

Owner

  • Name: Cefriel
  • Login: cefriel
  • Kind: organization
  • Email: info@cefriel.com
  • Location: Milano, Italy

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Dependencies

mapping-template/Dockerfile docker
  • openjdk 11-jdk build
mapping-template/docker-compose.yml docker
  • cefriel/mapping-template eval
morph-kgc/Dockerfile docker
  • python 3.8 build
morph-kgc/docker-compose.yml docker
  • cefriel/mapping-template morph
rml-mapper/Dockerfile docker
  • openjdk 11-jdk build
rml-mapper/docker-compose.yml docker
  • cefriel/mapping-template rmlmapper