ML Schema: Machine Learning Schema

Agnieszka Lawrynowicz

Poznan University of Technology, PolandOpenML2016

March 17, 2016

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W3C Machine Learning Schema Community Group

https://www.w3.org/community/ml-schema/

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Goals

To define a simple shared schema of data mining/ machine learning(DM/ML) algorithms, datasets, and experiments that may be used inmany 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 anddevelop 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 likeML tool developers

Apply for funding (e.g. EU COST, UK Research Councils,Horizon2020 Coordination and Support Actions) to organizeworkshops, and for dissemination

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Goals

Use this schema as a basis to align existing DM/ML ontologiesand develop more specific ontologies with specificpurposes/applications

Prevent a proliferation of incompatible DM/ML ontologies

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ML ontologies and vocabularies

OntoDM

DMOP

Expose

MEX vocabulary

others: KDDONTO, KD, DMWF, ...

mostly having several hundreds of classes, some highly axiomatized

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OntoDM

Pance Panov, Larisa N. Soldatova, Saso Dzeroski: Ontology of core data miningentities. 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 fordrug design, used by Eve Robot Scientist

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DMOP: Data Mining Optimization Ontology

C. Maria Keet, Agnieszka Lawrynowicz, Claudia d’Amato, Alexandros Kalousis, PhongNguyen, Raul Palma, Robert Stevens, Melanie Hilario: The Data Mining OPtimizationOntology. 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 IntelligentDiscovery Assistant for RapidMiner

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Expose

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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MEX vocabulary

Diego Esteves, Diego Moussallem, Ciro Baron Neto, Tommaso Soru, Ricardo Usbeck,Markus Ackermann, Jens Lehmann: MEX vocabulary: a lightweight interchange formatfor 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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Previous step towards aligning DM/ML ontologies

DMO Ontology Jamboree, Josef Stefan Institute, Slovenia, 2010

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Goals

To define a simple shared schema of data mining/ machinelearning (DM/ML) algorithms, datasets, and experiments thatmay be used in many di↵erent formats: XML, RDF, OWL,spreadsheet tables.

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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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Goals

Turn machine learning algorithms and results into linked opendata

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OpenML2016 plan for integrating OpenML with MLSchema 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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Goals

Collect use cases from the academic community and industry

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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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Goals

Promote the use of this schema, including involvingstakeholders like ML tool developers

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You are invited to join the W3C ML Schema group!

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