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Machine Learning Schema Community Group
Mission statement
ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments.
A targeted status for ML-Schema is: a community agreed schema as a basis for ontology development projects, markup languages and data exchange standards; and an extension model for the schema in the area of data mining and machine learning.
Goals of this group
- To define a simple shared schema of data mining/ machine learning (DM/ML) algorithms, datasets, and experiments that may be used in many different 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
Guidelines
- Open GitHub issues for any kind of discussion. Selected items will be discussed during telco's. For instance, see how this works for schema.org
- Store all schema in [http://www.w3.org/TR/turtle/](TTL format). This you can load in Protege, Jena,... if you want to edit them locally.
- Use the GitHub Wiki for information relevant to the group and users: https://github.com/ML-Schema/core/wiki
- Include structure (inheritance, value bounds) so arrive at a non-ambiguous schema.
- We use namespace 'mls' and will register this at PURL asap.
- If there is an issue to discuss, first create a GitHub issue, and then label it as 'discuss in call'
Conference calls
- We do a conference call on Hangout every two weeks, on Monday 13:30 - 14:30 (European time zone)
- Next calls 2016: Feb 1, Feb 15, Feb 29, ...
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