Re: [ontology-summit] [Bottlenecks] Identifying Bottlenecks in Ontology

[Ed Lowry <eslowry@xxxxxxxxxxxx>]

Matthew West asks:
>-          Why is it that ontological approaches are not taken when they
>could/should be?

A basic reason is that no common language for technical literacy has been
developed or even seriously considered as a possibility. On 7 leading edges
currently used languages are deficient compared with a design distributed
at IBM in 1973. See "Inexcusable Complexity for 40 years" on my web site.

Edward S. Lowry
Bedford Mass
eslowry@xxxxxxxxxxxx
http://users.rcn.com/eslowry 



On 1/20/2014 10:41 AM, Quentin Reul wrote:

Hi Matthew,

I work as a Content Semantics architect at Wolters Kluwer, where we are developing ontologies to represent documents and their metadata. As part of this work, I have experienced difficulties (or bottlenecks) in the application of ontologies. 

Please find attached my answers to your questions:

• What is it that takes a lot of time and effort? There are 2 tasks that are rather time-consuming; (i) the extraction of the knowledge from Subject-Matter experts (SMEs), and (ii) the explanation of the model to developers using it. 
  
• What is it that is very expensive? The extraction of the knowledge is expensive as most SMEs have a "ideal" vision of what their knowledge is and tend to differ from the actions / reasoning they actually use. For instance, doctors will answer questions theoretically when their reasoning while consulting patients tend to be different.
  
• What is it that is held up because of a lack of scarce resources? I am not quite sure what you refer to as "scarce resources"
  
• Why is it that ontological approaches are not taken when they could/should be? Time constraint on the delivery of the ontological artifacts mean that the model and its implementation are generally not separated. 
  

Kind regards,

Quentin Reul

www.linkedin.com/in/quentinreul

On 18 January 2014 03:33, Matthew West <dr.matthew.west@xxxxxxxxx> 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 C of the programme is: "Overcoming Ontology Engineering Bottlenecks"

http://ontolog.cim3.net/cgi-bin/wiki.pl?OntologySummit2014_Overcoming_Ontolo
gy_Engineering_Bottlenecks_Synthesis



So to kick Track C off, we would like to invite you to help us identify the
key bottlenecks in ontology engineering for Big Data and the Semantic Web.



-          What is it that takes a lot of time and effort?

-          What is it that is very expensive?

-          What is it that is held up because of a lack of scarce resources?

-          Why is it that ontological approaches are not taken when they
could/should be?



Please let us know on this thread, and/or add to our community input page:

http://ontolog.cim3.net/cgi-bin/wiki.pl?action="">
<http://ontolog.cim3.net/cgi-bin/wiki.pl?action="">
vercoming_Ontology_Engineering_Bottlenecks_CommunityInput>
&id=OntologySummit2014_Overcoming_Ontology_Engineering_Bottlenecks_Community
Input



Regards



Matthew West, Pascal Hitzler, Krzysztof Janowicz



Track C Co-Champions





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--
Ed Lowry

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