The document outlines the Machine Learning Schema (ML Schema) developed by Agnieszka Lawrynowicz and aims to create a shared framework for data mining and machine learning algorithms, datasets, and experiments in various formats. It seeks to align existing ontologies, support the integration of machine learning algorithms into linked open data, and promote collaboration with stakeholders like ML tool developers. Use cases include experiment sharing, meta-learning, and improving reproducibility in machine learning publications.

1. ML Schema: Machine Learning Schema
Agnieszka Lawrynowicz
Poznan University of Technology, Poland
OpenML2016
March 17, 2016
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2. W3C Machine Learning Schema Community Group
https://www.w3.org/community/ml-schema/
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3. Goals
To define a simple shared schema of data mining/ machine learning
(DM/ML) algorithms, datasets, and experiments that may be used in
many di↵erent formats: XML, RDF, OWL, spreadsheet tables.
Collect use cases from the academic community and industry
Use this schema as a basis to align existing DM/ML ontologies and
develop more specific ontologies with specific purposes/applications
Prevent a proliferation of incompatible DM/ML ontologies
Turn machine learning algorithms and results into linked open data
Promote the use of this schema, including involving stakeholders like
ML tool developers
Apply for funding (e.g. EU COST, UK Research Councils,
Horizon2020 Coordination and Support Actions) to organize
workshops, and for dissemination
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4. Goals
Use this schema as a basis to align existing DM/ML ontologies
and develop more specific ontologies with specific
purposes/applications
Prevent a proliferation of incompatible DM/ML ontologies
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5. ML ontologies and vocabularies
OntoDM
DMOP
Expos´e
MEX vocabulary
others: KDDONTO, KD, DMWF, ...
mostly having several hundreds of classes, some highly axiomatized
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6. OntoDM
Pance Panov, Larisa N. Soldatova, Saso Dzeroski: Ontology of core data mining
entities. Data Min. Knowl. Discov. 28(5-6): 1222-1265 (2014)
built in compliance to upper level ontologies BFO, OBI, IAO, modularized
incorporates structured data mining
Use case: generic, middle level ontology for ML; representing QSAR entities for
drug design, used by Eve Robot Scientist
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7. DMOP: Data Mining Optimization Ontology
C. Maria Keet, Agnieszka Lawrynowicz, Claudia d’Amato, Alexandros Kalousis, Phong
Nguyen, Raul Palma, Robert Stevens, Melanie Hilario: The Data Mining OPtimization
Ontology. J. Web Sem. 32: 43-53 (2015)
development started in e-LICO EU FP7 project (2009-2012)
detailed algorithm internal characteristics (’qualities’)
Use case: meta-learning (’whitebox’), meta-mining, used to produce Intelligent
Discovery Assistant for RapidMiner
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8. Expos´e
Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geo↵rey Holmes:
Experiment databases - A new way to share, organize and learn from experiments.
Machine Learning 87(2): 127-158 (2012)
re-uses OntoDM (at top-level) and DMOP (at bottom level)
superseded by OpenML DB schema
Use case: experiment databases, ExpML markup
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9. MEX vocabulary
Diego Esteves, Diego Moussallem, Ciro Baron Neto, Tommaso Soru, Ricardo Usbeck,
Markus Ackermann, Jens Lehmann: MEX vocabulary: a lightweight interchange format
for machine learning experiments. SEMANTICS 2015: 169-176
lightweight interchange format
maps to PROV
Use case: annotating ML experiments and interchanging ML metadata
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10. Previous step towards aligning DM/ML ontologies
DMO Ontology Jamboree, Josef Stefan Institute, Slovenia, 2010
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11. Goals
To define a simple shared schema of data mining/ machine
learning (DM/ML) algorithms, datasets, and experiments that
may be used in many di↵erent formats: XML, RDF, OWL,
spreadsheet tables.
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12. The current draft of ML Schema
OpenML2016, Lorentz Center, Netherlands, 2016
(our work may be found at https://github.com/ML-Schema/core)
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13. Goals
Turn machine learning algorithms and results into linked open
data
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14. OpenML2016 plan for integrating OpenML with ML
Schema and Linked Data
Assign URIs to OpenML classes and properties
Align OpenML vocabulary to ML-Schema
Complete an initial specification of ML-Schema v1.0
Develop a tool to provide each OpenML entity with RDF data
(JSON-LD)
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15. Goals
Collect use cases from the academic community and industry
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16. Use cases
Experiment/model sharing
Workflow design/planning
Meta learning
Text mining
Experiment reproducibility in publications
Comparison of ML algorithms
Education
Call for use cases!
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17. Goals
Promote the use of this schema, including involving
stakeholders like ML tool developers
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18. You are invited to join the W3C ML Schema group!
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