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

["John Yanosy Jr." <jyanosyjr@xxxxxxxxx>]

Some thoughts from my experiences in creating ontologies.

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

1. Identifying the scope of knowledge to be represented and the context of use or application.

2. Defining Concepts is somewhat easier than defining useful relationships that structure the ontology model.

3. Refining the ontology during development to satisfy logical consistency

4. Modifying an ontology to capture expanding knowledge while ensuring logical consistency.
 -          What is it that is very expensive?

1. Refining and reviewing the ontology to satisfy requirements 

2. Integrating an ontology to an existing enterprise and software architecture 

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

1. An overarching data architecture with long term evolution and application of consistently defined concepts that can be reused with evolving and new services and applications

2.
 -          Why is it that ontological approaches are not taken when they
could/should be?
1. Lack of knowledge about the importance of an overarching data model and the role that semantics plays in defining  and offering a consistent interpretation of shared data among applications and services across the enterprise and across systems..

I have used natural language tools that can semi-automatically create an ontology from text sources, but refining the results of tens of thousands of concepts into a consistent model is hard. An ontology by its nature has some logical formalism that enables logical reasoning, but for this to work the ontology has to satisfy these constraints which NLP tools are deficient in. Also the extraction relies on the provenance of the text sources so garbage in garbage out again.

Best regards,

John

On Mon, Jan 20, 2014 at 2:24 PM, Ed Lowry <eslowry@xxxxxxxxxxxx> wrote:

John
RDF, like all of our currently used precise languages, failed to solve the
basic problem of combining flexible data structures
with simple plural expressions. It is behind on all 7 leading edges.
Ruby makes a start in the right direction by moving most iteration
code into declarations.
Ed

On 1/20/2014 3:03 PM, John McClure wrote:

Ed, interesting paper - have you considered the Resource Description Framework (RDF) in this regard, ie as an efficient information representation mechanism? Does RDF help or hinder the elimination of complexity you've noted in languages to-date? thanks/jmc

On 1/20/2014 10:54 AM, Ed Lowry wrote:

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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Peace be with you,
John A. Yanosy Jr.

Mobile: 214-336-9875

eMail: jyanosyjr@xxxxxxxxx

LinkedIn: www.linkedin.com/in/jyanosyjr

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