Re: [ontology-summit] [VarietyProblem] Tackling the Variety Problem in B

[Bart Gajderowicz <bartg@xxxxxxxxxxxxxxx>]

Hi Ken,

I can purpose two ideas which relate to machine learning, data mining, and their relation to ontologies.

1) My MSc work looked at extending ontologies with machine learning, for the purpose of ontology mapping. This can be applied to merging datasets which have associated ontologies.

http://www.scs.ryerson.ca/~bgajdero/msc_thesis/

I'd be happy to provide more information.

2) I'm not sure if this would fall under the Variety Problem domain, but the issue of asking the right questions of the date is an important aspect of data science (many other labels for this field but I'm choosing this one for now).

Understanding the underlining semantics of days points, data records, etc, allows a data scientist to:

- Ask the right questions, which is important when configuring statistical and data-mining models, as well as the experiments.

- Interpret experiment results in various ways.

- Draw conclusions which may lead to better understanding of the data, as well as better questions for the next iteration of experiments.

--
Bart Gajderowicz
PhD Candidate, 2017
Mechanical and Industrial Engineering
University of Toronto
http://bartg.org
   

On 24 Jan 2014 14:50, "kenb" <kenb@xxxxxxxxxxx> wrote:

Dear Colleagues,

This year's Ontology Summit

http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2014

has as its topic: "Big Data and Semantic Web Meet Applied Ontology".

Track D is: "Tackling the Variety Problem in Big Data"

The Synthesis Page is at:
http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2014_Tackling_Variety_In_BigData_Synthesis

So to kick off Track D, we would like to ask:

- What can ontologies best contribute to Big Data and how can this be done?

Here are some suggestions from the Synthesis Page:

Background knowledge of the domain within which the data is being produced, processed and analyzed

The structure of the data

Provenance of the data, including any transformations, analyses and interpretations of the data that have been performed

Processing workflows, including merging and mapping data from multiple sources

Privacy concerns

Other suggestions?

- What aspects of the variety problem would have the most benefit in the
short term? In the long term?

- What use cases would be the most compelling?

Please let us know your thoughts as replies to this email. Please also
feel free to add ideas, pointers etc. to the Community Input page for
Track D, at:

http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2014_Tackling_Variety_In_BigData_CommunityInput

Best regards,

Ken Baclawski and Anne Thessen
Track D Co-champions

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