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mlso

The Machine Learning Sailor Ontology

https://github.com/dtai-kg/mlso

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.2%) to scientific vocabulary

Keywords

big-data data-management knowledge-graph machine-learning ontology
Last synced: 6 months ago · JSON representation ·

Repository

The Machine Learning Sailor Ontology

Basic Info
  • Host: GitHub
  • Owner: dtai-kg
  • License: apache-2.0
  • Default Branch: main
  • Homepage: http://w3id.org/mlso
  • Size: 2.61 MB
Statistics
  • Stars: 6
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
big-data data-management knowledge-graph machine-learning ontology
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License Citation

README.md

MLSO

License DOI

Available at: http://w3id.org/mlso


Scope of the Ontology and Quick Start

The Machine Learning Sailor Ontology (MLSO) and the Machine Learning Sailor Taxonomies (MLST) provide a flexible schema to represent ML pipelines, datasets, implementations and experiments.

This repository contains the Turtle files and the documentation for the MLS ontology and taxonomies. MLSO is an ontology for describing machine learning datasets, tasks, pipelines, experiments, software and publications. The ontology extends ML-Schema, DCAT, FaBiO and SDO, complemented by 8 taxonomies formulated as controlled vocabularies.

Quick Start: Check out MLSO's documentation, the Turtle files for the ontology and the taxonomies.



Core Entities and Relationships of MLSO: Error loading the image!



MLSO and MLST Namespaces:

| Module | Description | Namespace | |:---------:|:---------:|:---------:| | MLSO | Machine Learning Sailor Ontology | http://w3id.org/mlso | | MLSO-DC | Dataset Characteristic Taxonomy | http://w3id.org/mlso/vocab/datasetcharacteristic | | MLSO-FC | Feature Characteristic Taxonomy | http://w3id.org/mlso/vocab/featurecharacteristic | | MLSO-EM | Evaluation Measure Taxonomy | http://w3id.org/mlso/vocab/evaluationmeasure | | MLSO-EP | Estimation Procedure Taxonomy | http://w3id.org/mlso/vocab/estimationprocedure | | MLSO-LM | Learning Method Taxonomy | http://w3id.org/mlso/vocab/learningmethod | | MLSO-ALGO | Algorithm Taxonomy | http://w3id.org/mlso/vocab/mlalgorithm | | MLSO-F | Machine Learning Field Taxonomy | http://w3id.org/mlso/vocab/mlfield | | MLSO-TT | Task Type Taxonomy | http://w3id.org/mlso/vocab/mltask_type |



Content negotiation at w3id.org

The ontology is published using GitHub pages. Content negotiation configuration on w3id.org is available here.



Cite

Thank you for reading! To cite our resource:

@InProceedings{dasoulas2024mlsea,
    author    = {Dasoulas, Ioannis and Yang, Duo and Dimou, Anastasia},
    booktitle = {The Semantic Web},
    title     = {{MLSea: A Semantic Layer for Discoverable Machine Learning}},
    year      = {2024}
}

Owner

  • Name: dtai-kg
  • Login: dtai-kg
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
title: "MLSea"
license: Apache-2.0
authors:
  - family-names: Dasoulas
    given-names: Ioannis
preferred-citation:
  authors:
    - family-names: Dasoulas
      given-names: Ioannis
    - family-names: Yang
      given-names: Duo
    - family-names: Dimou
      given-names: Anastasia
  title: "MLSea: A Semantic Layer for Discoverable Machine Learning"
  type: conference-paper
  collection-title: "Proceedings of the 21\textsuperscript{th} Extended Semantic Web Conference (ESWC)"
  year: 2024

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