ONT Re: Data Models, Ontologies, Logic
o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
This is an important issue. I wish that I could tell you how long
I've worked on aspects of it, but it's been too long to recall how
it all got started. Most of the real paying academic jobs I've had
from student days till I took off again to go back to school in 1992
kept my nose eyeballs deep in real data, in vast quantities, gathered
at great expense, by people who had some real need of it, and all of
whom were under the illusion that this data referred to significant
aspects of our common existence that we needed to make critical
decisions about, so they cared about how to analyze it right,
and that is what I eventually grew into doing. Now it ain't
cheese sandwiches, but it's a kind of reality all the same.
A good deal of this time I acted as a buffer between
quantitative researchers and qualitative researchers.
Since both groups understood what research was about,
and the qualitative thinkers were required to garner
more standard statistics than the quantitative folks
were forced to take linguistic and logical methods,
I tended to spend more time trying to convince the
quantitative types that they needed to build more
logic into their statistical analysis packages.
Consequently, I was under the impression for the longest time in the SUO group
that it would merely be a matter of advancing the other side of the pons a bit.
But I forgot the part about "understanding what research is about", including
the overall empirical attitude and the non-trivial math that goes into stats.
Now maybe it's just our peculiar sample, but this assumption does not hold
for the population that I initially labelled as the "Qual" group here, who
seem to be a brand of logicians who have no real sense of empirical data
and no real acquaintance with math and stat as they are actually used.
To put it mildly,
Jon Awbrey
P.S. The preceding is prologue.
I'll get to the specifics
of this topic head later.
J.A.
o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o
Jim Farrugia wrote:
>
> Matthew, Adam, et al.,
>
> In response to a recent posting by Matthew (in response to Adam)
> on the SUO list, I am posting this to ONT instead, but feel free
> to kick it back up to SUO if you think it appropriate.
>
> (Note, I see this just crossed John's post to the SUO list ...)
>
> Matthew says:
>
> <little snip>
>
> > MW: An ontology is really overkill for this. Though I should perhaps make
> > clear some distinctions I use related to terms like ontology.
> >
> > - Taxonomy, a dictionary of standard terms and their natural language definitions.
> >
> > - Thesaurus, a taxonomy with subtype/supertype relations.
These are not standard uses of these words. And you can look it up.
Taxonomies in well-developed fields depart significantly from lists
of common names in ordinary language nomenclature. And that is the
first time I have ever heard that usage of "thesaurus".
> > - Data Model, a structure of types and relations against which instances
> > of the types and relations can be stored. Definitions of the types and
> > relations are in natural language. Some constraints are defined, usually
> > in terms of the cardinality of relationships.
> >
> > - Ontology, types, relations, and possibly instances of those together
> > with axioms, usually in First Order Logic, that define the rules that
> > apply to members of the types and relations.
> >
> > Data Models are what are usually developed/used to design a database.
> > The axioms of an ontology are generally of little use/relevance either
> > in the design of the database, or in the population of it by instances.
> >
> > So in general my question for this sort of case would be: Why would you
> > develop an ontology (more work) when a data model will do?
>
> <big snip>
>
> 1. Would people on this list agree with Matthew's distinctions between
> data models and ontologies?
>
> On the one hand, it does seem that the key distinction is the use of
> axioms, which in turn allow for inferences above and beyond what might
> be stored in a database (based on a data model). On the other hand, one
> often encounters, in doing data modeling, certain constraints, which
> are, in effect, "rules that apply to members of the types and
> relations." Is there a sharp distinction between the kinds of rules
> used to define constraints in data models and the kinds of rules defined
> via axioms in ontologies?
>
> 2. And what is the role of logic in defining these rules (for data models
> and for ontologies)? I ask the question broadly, but one particular issue
> I am interested in is the use of implications (if-then statements) to
> express these rules. For the database case especially, such rules seem
> to be expressed as implications. Similarly, I think, for the ontology
> case. But typically the logic used in an ontology goes much further, or
> so it seems to me. For instance, won't any logic used to represent
> an ontology also have pegged to it inference rules, a proof theory,
> and a semantics? Do all these logical extras exist also in, say, a
> relational data model, and is the reason they are not so apparent is
> that the relevant subset of first-order logic is in essence hard-wired
> and implicit in the relational model?
>
> Could someone elaborate on the different ways in which logic is used to
> define the rules both in data models and ontologies, perhaps especially
> focusing on the common logical flavor of if-then rules in both cases?
>
> 3. Related to the second point above, as well as to the SUO's effort in
> creating an ontology, can one express the kind of ontology we are after
> _without_ using logic? That is, for the kind of computer-crunchable
> ontology that the SUO effort seeks to create, could it exist without
> logic? I suspect that it could, but that such an ontology would be an
> impoverished one.
>
> Two more, if I may.
>
> 4. Related to the third point, is a logic-based, machine-processable
> representation of an ontology particularly apt for our purposes, for
> the following reasons?
>
> One could say that ontology is concerned with "what is," the things that
> exist and the relations among those things. Logic (plus) model theory is
> an appropriate way to express the notions of an ontology, because the syntax
> of logic can represent things that are, and the model theory of a logic allows
> us to say "what is so." Does this make any sense?
>
> 5. Finally, related to the points three and four above, and to some recent
> discussion on the SUO list about the language for the SUO (which I think
> have been raised before -- and settled????), I have a question for
> Adam (and John and Chris and whoever else cares to chime in).
>
> Simply put, "Where is the semantics of SUMO?"
>
> Don't clobber me; I think I may have an inkling of the answer, but I need
> some help to make things clearer to me. My inkling is: "Well, the semantics
> of SUMO is given, essentially, via the semantics of KIF (or the version
> that SUMO uses)."
>
> The semantics of KIF (to be superseded by the semantics of CL) -- well,
> there is where I have a question. On the one hand, there is a model theory
> for KIF at http://logic.stanford.edu/kif/semantics.tex
>
> On the other hand, accordinig to the draft standard at
> http://logic.stanford.edu/kif/dpans.html
>
> "The language has declarative semantics. It is possible to understand the
> meaning of expressions in the language without appeal to an interpreter
> for manipulating those expressions."
>
> I'm guessing that KIF's semantics is defined by the model theory document,
> but how to account for the above mention of a "declarative semantics"?
>
> Thanks,
>
> Jim
>
o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o~~~~~~~~~o