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

[Quentin Reul <Quentin.H.Reul@xxxxxxxxx>]

Hi,

We are essentially dealing with migrating content from XML to a world where the true semantics of the underlying content can be expressed. When started our investigation, standards such as FRBR [1] were not very well defined, while ontologies (such as DoCO [2]) were not yet available. As a result, we created our (proprietary) own model to represent content and still looking at determining the best approach to document the ontology. We have tried using a simple graphical notification, UML, and HTML (generated using tools such as LODE [3]). Sadly, there is no tools that can provide the info for different type of users without having to spend hours on the documentation (which is a huge bottleneck at present).

In terms of natural language tools, I investigated the use of controlled natural language to express ontological knowledge, but failed to find an approach to easily express ontological axioms. I have also tried to use Fluent Editor [4], but I found it rather counter-intuitive and the import was problematic for highly modular ontologies (such as the one we have developed).

Thanks,

Quentin

[1] http://www.ifla.org/publications/functional-requirements-for-bibliographic-records

[2] http://purl.org/spar/doco

[3] http://www.essepuntato.it/lode

[4] http://www.cognitum.eu/semantics/FluentEditor2/Default.aspx

On 20 January 2014 10:57, Mills Davis <mdavis@xxxxxxxxxxxxxx> wrote:

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

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

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

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