Re: [ontolog-forum] FW: Fundamental questions about ontology use andreus

["Godfrey Rust" <godfrey.rust@xxxxxxxxxxxxx>]

Thanks Sean for a clear positioning which I would endorse: in the domains we work in I have found the approach that "an ontology is a smart, flexible data model" gives people a place they can be comfortable working from. It means, though, that the ontology you are using has to be presentable to non-ontologists or logicians as a coherent data model in a form that they can see is an interpretation of their business or domain, and that's part of John's "making the box" problem, because not much attention has been paid to that to date.

 

Godfrey

 

Godfrey Rust
Chief Data Architect
Rightscom/Ontologyx

----- Original Message -----

From: "Sean Barker" <sean.barker@xxxxxxxxxxxxx>

To: "Ontolog-Forum-Bounces" <ontolog-forum@xxxxxxxxxxxxxxxx>

Sent: Monday, June 22, 2009 9:23 PM

Subject: [ontolog-forum] FW: Fundamental questions about ontology use andreuse

John,

There is a certain amount of doubt in the 
businesses I work for as to
whether ontologies are anything more than hype. 
The approach I take is to
say the following:

1) An ontology is a type 
of data model. It differs from a conventional data
model in that it is 
written in logic.

2) In conventional data models, the model is embodied 
as special purpose
code that has to be created specifically for the model. 
With an ontology,
one uses a general purpose reasoner that works directly 
from the logic
representation.

3) If you want to interoperate between 
two systems with different data
models, you need to create a mapping between 
them. With conventional models,
this mapping must be turned into special 
purpose mapping code, whereas with
an ontology, the mapping is also written 
in logic, and so the same general
purpose reasoner can be used.

4) The 
advantage of ontologies is therefore that the model and the mappings
are data 
for a general purpose reasoner, rather than requiring the
generation of 
special purpose code. This makes it easier to maintain
interfaces, since the 
updates can be sent out as data rather than code
patches. (Assume here that 
there may be many hundreds of systems that carry
the interface).

The 
business advantage of an ontology over a conventional data model is
therefore 
likely to be in the costs of maintaining interfaces.

Note: The biggest 
cost of an ontology is confirming its grounding - that is,
confirming that 
the data means what you think it means. A failure to do that
may mean that 
the systems may need to be closed down for two or three days
to recover them 
to a safe state - for example, in one data exchange,
importing bad data could 
have stopped 5,000 people working for two or three
days (say £2-3 million per 
day). Since most of the data I deal with is high
value, there is a high risk 
if you get it wrong - which means working closed
worlds so that you can trust 
the data sources.

The question is not, "Why do we need ontologies", since 
there are plenty of
ways to do the same thing without ontologies. Rather, the 
question is, can
we do what we want to do in a cheaper, more reliable 
way?

The biggest challenge is to make ontologies interesting, in the 
sense that
they reveal the ideas driving their usage. In the teaching of 
mathematics,
at least at the higher levels, the function of proof is to 
reveal the
mathematical ideas used, rather than merely confirm a fact. At the 
moment, I
don't know how to do that in an ontology - it was bad enough in 
formal
specifications. Rather, we are looking at how to do the things we want 
in a
conventional (though not very conventional) data model, and once we know 
how
they work, transforming them to an ontology.

Sean 
Barker
Bristol, 
uk


 
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