but we need a common
way to describe our work allowing the mediation of viewpoints. As our
worldviews differ in scope (what we look at), resolution (detail we
are looking at), and structure (categorization of what we are looking
at), these mediations will not always be loss-free, but that is part
of the nature of the beast.
It seems like we are
starting to come to very similar observations and reach mappable
conclusions in different scientific domains.
Andreas
Bravo, Rich – this is
the first time I’ve heard anyone in any of these ontology/SUO forums
stress so strongly the human-factor aspect of data semantics.
I’ve been trying to argue this point for years but to most
CS-trained individuals it just falls on deaf ears. I even
have a nice little catchy name for the theory: “Data Is
Speech”. As you suggest, there will be multiple
ontologies (or whatever you want to call them) to formally represent
different views of the word and they will need to be quickly adaptable
to changing business requirements . And the one significant
missing and way way underserved ingredient is mapping and translation
technology.
Bill
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
Dave McComb wrote:
Ontologies, in my
mind, offer a way to help sort, categorize and organize the chaos
we've created. We have to integrate the old with the new as we
go forward, but this isn't as hard as it sounds. SOA has given
us the general technological approach, Semantics is adding a layer of
rationalization on top.
Nicely stated - I'm still reading Karl Popper's Logic of
Scientific Discovery, which is a dramatic reminder of the
subjectivity we brush aside so easily. Remember that the people
who entered all that data into the database in the first place were
each individuals with their own internal ontologies.
The first problem in any database, even prior to formalizing “the”
ontology or (more effectively, “some” ontologies) is to find ways to
ascertain the meaning of data recorded there. I described that
in detail on my web site at:
www.englishlogickernel.com-Patent-7-209-923-B1.PDF
For example, when a Yes/No answer is mixed with 1/0, 2/1, T/F,
True/False, and MIXTURES of the above (yes, T/1/F/0, 2/1/0 and other
mixtures are possible since people are not consistent systems).
Attempts to force fit the answer into a very precise type of
form (T/nil) leads to frustrated users, GUI programming errors,
confused analysts and lots of data entry errors because most users
don't have a real stake in most systems they deal with.
For a few lucky enterprises, there may have been "the" enterprise
ontology by designers who thought it might be useful. In my
experience, every enterprise system database evolves faster than the
IT staff allocated to manage it. There is too big a loop between
the user with her needs and the developers who make changes.
Meaning is in the eyes of the people who provide the data, and lots
of that meaning is subject to human judgment, valuation diversity, and
just plain old personal preferences. Then there is the meaning
in the perceptions of data analysts who try to make sense of the user
data, or find patterns there, typically not having the original users
available at the analyst's moment of investigation.
But between the data entry person and the analyst, there may be
lots of other users reading, perceiving, populating, editing, and
otherwise in their own eyes "adding" meaning to the data by changing
the original source data cells – all to meet their own individual
ontologies. So the typical enterprise database is full of
classes and properties that shouldn't be there (given “the” ontology),
but in fact they are. Even worse, the variations are the main
source of information in businesses looking for ways to improve
profit, service, quality or other metrics. The changes in data,
the variations, contain the most information.
Education and training of staff to enter data "the right way" is a
hopeful tactic, but almost a waste of time, and users mostly still do
what they think is good on the spur of the moment, just like the rest
of us. People work in our own conceptual ways, we deal with
everyday situations in our own lexicon, grammar and thought processes,
and "education" applied in that way is more appropriately called
"indoctrination". It tries to “fix” the users’ dynamic flow of
structural information instead of adapting to that changing flow by
processing a changing ontology with changing projected user
ontologies.
So the only conclusion I can reach is that "the" enterprise
ontology, if singular, is a dynamic and variable entity that is no
more fixed than any other specification to be implemented real soon
now. Forget about selecting ONE, and expect multiple ontologies;
the transition sequencing from one to another (the periodic version
update) is likely to become more manageable that way. Expect
ontologies to be iterative and plural, not fixed and singular.
I think every user might some day have her own ontology.
Localizations and personalization can be used to adapt "the"
ontology to a wider range of individual user needs as much as writing
specialized queries in SQL which takes development labor.
Surely a "semantic" application will influence the user's GUI
behaviors in some dynamic way. So if "the" ontology is dynamic,
then "her" ontology must be getting calculated from "the" ontology
either very quickly or very incrementally to meet GUI performance
requirements.
JMHO,
-Rich