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[ontolog-forum] Where UML models fit

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "henson" <henson.graves@xxxxxxxxxxx>
Date: Mon, 17 Feb 2014 11:35:48 -0600
Message-id: <BLU176-DS8F6D3F46F076839B38455E4990@xxxxxxx>
Ken
 
UML and SysML models are commonly used in engineering for both representation, i.e., description of some part of the physical world and as specifications for things to be built, as well as for reasoning. Many engineers still think of these models as purely representational, but increasingly they are being used for reasoning, or at least engineers are becoming to understand the potential to solve engineering problems with automated reasoning.  To be useful for either purpose the models have to be built with attention the same practices one finds for ontology engineering. While languages such as UML do not come with a formal semantics, there are embedding results for fragments of UML into Description Logic and other logic-based formalisms. For example, a UML class model can be embedded as an axiom set in DL. Certainly a formal semantics is required if reasoning results are to be trusted.
 
These embedding results give credence to the idea that a UML engineering model can be viewed as an axiom set. As an axiom set an engineering model may have multiple models, or no models, in the logic sense of the word. The UML models are more specific than most ontologies, but they can be though of as unformalized ontologies. Upper level ontologies such as DUL have been imported into UML as class models and used successfully in model development.
 
Henson Graves
 
From: Ken Laskey
Sent: Saturday, February 15, 2014 10:04 AM
Subject: Re: [ontolog-forum] What the difference re., Data Dictionary,Ontology, and Vocabulary?
 
Let me bring in a couple examples from non experts and see whether we can apply the debated definitions to clarifying the real world.
 
When I am presented with what someone is calling an “ontology”, I often ask whether it is meant for representation or reasoning.  Often, the formalism of choice is UML.  Sometimes the UML tries to exhaustively capture relationships, cardinalities, … and sometimes there is a lonely class diagram with a self-referencing “uses” followed by a page of text.  Both of these can provide a level of representation but little, if any, automated reasoning.
 
Where does this range of UML models fit?  How can we make that clear to the corresponding practitioners without turning them into experts or berating them for not being experts.
 
Ken
 
-------------------------------------------------------
Dr. Kenneth Laskey
MITRE Corporation, M/S H305          phone: 703-983-7934
7515 Colshire Drive                             fax: 703-983-1379
McLean VA 22102-7508
 
On Feb 14, 2014, at 8:19 PM, David Eddy <deddy@xxxxxxxxxxxxx> wrote:

Kingsley -
 
On Feb 14, 2014, at 7:47 PM, Kingsley Idehen wrote:

I have spent more than 20+ years of total dedication to making new and emerging technologies work with existing (so called legacy) systems. I founded OpenLink Software to enable integration of data across artificial data silos, created by *myopic* applications. 
 
But that's the DATA the systems produce.
 
 
I trying to talk about the SYSTEMS, that produce the data.
 
The SYSTEMS & the DATA are not the same thing.
 
 
The SYSTEMS are the machine tools that produce the end product, DATA.
 
 
I am reminded of wisdom from the 1840s when industrial America was learning how to make things.  It was noticed that building quality into the manufacturing process is far more efficient than inspecting defects out.
 
Just handling the data—the manufactured product—is an exercise in futility until a firm understanding of the upstream manufacturing process is fully understood.
 
If one doesn't know which systems, programs, logic, data structures & rules are producing the data, how will one know when the data suddenly changes?
 
 
The sort of data dictionary product I'm talking of—decidedly not a list of data elements—enables organizations to do impact analysis—what's connected to what—so that we're not constantly repairing down-stream errors.
 
 
 
 
integration of data across artificial data silos,

Integration is not possible with silos.  You mean interoperable.  There's a huge difference.
 
Integration is possible when one is in command, owns the budget & has a blank sheet of paper, otherwise the only option is interoperability.
 
 
The silos are not necessarily artificial.  They were built that way for valid reasons... available skills, limits of technology, budget, vision, deadlines, organizational boundaries, etc.
 
Silos are a complex reality we're going to have to learn to deal with... as John Sowa points out, these systems are going to be with us for decades.
 
 
 
Understanding language & meaning across Silos would be an extremely useful application for Ontologies if they can be commercialized.
 
____________________________
David Eddy
Babson Park, MA

deddy@xxxxxxxxxxxxx
 

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