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

To: Quentin.H.Reul@xxxxxxxxx
Cc: Ontology Summit 2014 discussion <ontology-summit@xxxxxxxxxxxxxxxx>
From: Mills Davis <mdavis@xxxxxxxxxxxxxx>
Date: Mon, 20 Jan 2014 11:57:08 -0500
Message-id: <74EF6BE0-5B72-4312-A2F7-9F5E0632260A@xxxxxxxxxxxxxx>
Quentin,

Thanks. The practitioner's observations you share have good company throughout the history of AI. For example, knowledge engineers  in the 80s expressed similar concerns when building expert systems.  Similarly, the goal of accelerating the process of and driving down the cost to extract and model (executable) knowledge was one of the motivations that lead Paul Allen to fund research at Vulcan.

Question:  what has been the experience at Wolters Kluwer with using natural language based tools to gain leverage on the process of knowledge extraction?  (For example, capturing sources that an expert would consult, as well as eliciting knowledge directly from the SME...)

Mills Davis

On Jan 20, 2014, at 10:41 AM, Quentin Reul <Quentin.H.Reul@xxxxxxxxx> 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


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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Mills Davis
Managing Director
Project10X
202-667-6400
202-255-6655 cel
skype: millsdavis
mdavis@xxxxxxxxxxxxxx





Mills Davis
Managing Director
Project10X
202-667-6400
202-255-6655 cel
skype: millsdavis
mdavis@xxxxxxxxxxxxxx






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