W3C home > Mailing lists > Public > semantic-web@w3.org > October 2016

Re: [ANN] Machine Learning Schema Core Specification

From: Phil Archer <phila@w3.org>
Date: Tue, 18 Oct 2016 09:11:43 +0100
To: Agnieszka Ławrynowicz <agnieszka.lawrynowicz@cs.put.poznan.pl>, SW-forum <semantic-web@w3.org>, Sebastian Hellmann <hellmann@informatik.uni-leipzig.de>
Message-ID: <733158f1-2c38-85c3-fe41-bd78e31f10fe@w3.org>
Congratulations Agnieszka and all!

I'll be interested to see the response you get from this. It is related, 
obviously, to Sebastian Hellmann's work on NIF [1] and, subject to 
community and Member support/interest, the two *might* form the basis of 
a future full W3C WG.

Meanwhile, if you want me to install your turtle file at w3.org/ns/mls, 
let me know.



[1] http://persistence.uni-leipzig.org/nlp2rdf/

On 18/10/2016 01:32, Agnieszka Ławrynowicz wrote:
> We are happy to announce the availability of
>            ***Machine Learning Schema Core Specification*** release October 17, 2016
> a simple shared schema that provides a set of classes, properties, and restrictions that can be used to represent and interchange information on data mining and machine learning algorithms, datasets, and experiments.
> This lightweight schema may be used as a basis for ontology development projects, markup languages and data exchange standards. In particular, it aims to align existing machine learning ontologies and to support development of more specific ontologies with specific purposes/applications. The main purpose is to increase interoperability by preventing a proliferation of incompatible machine learning ontologies as well as to provide a high-level standard to represent machine learning data.
> The schema also defines a relationship between machine learning algorithms and their single executions and experiments and studies encompassing them. It aims at stimulating the development of standards in order to achieve high level of interoperability among scientific experiments concerning machine learning. By facilitating the metadata interchange process, the ML Schema may foster reproducible research. Another goal of ML Schema related to interoperability and reproducible research is to facilitate turning machine learning algorithms and results into linked open data.
> The further information may be found at:
> http://ml-schema.github.io/documentation/ <http://ml-schema.github.io/documentation/>
> Let us know how you would like to use ML Schema:
> https://github.com/ML-Schema/core/wiki/UseCases <https://github.com/ML-Schema/core/wiki/UseCases>
> Regards and cheers,
> The W3C ML Schema Community Group
> Website:
> https://www.w3.org/community/ml-schema <https://www.w3.org/community/ml-schema>
> Github:
> https://github.com/ML-Schema/core <https://github.com/ML-Schema/core>
> Twitter
> https://twitter.com/ml_schema <https://twitter.com/ml_schema>


Phil Archer
W3C Data Activity Lead

+44 (0)7887 767755
Received on Tuesday, 18 October 2016 08:11:46 UTC

This archive was generated by hypermail 2.3.1 : Tuesday, 18 October 2016 08:11:49 UTC