Dr
Gruber is one who did most contribution in promoting the merits of ontology in
information science and technology, even just giving an ostensive
definition: ?an explicit specification of conceptualization?. It
triggered a logical chain reaction of redefinitions: ?a theory of content?, ?a
theory (a system) of concepts/vocabulary?, ?a set of agreements about a set of
concepts?, ?the representation of the semantics of terms and their
relationships?; ?the class hierarchy?, ?a complete schema of the domain
concepts?, ?an entity-relationship schema with subsumption relations between
concepts?; ?conceptual patterns?, ?concept heterarchies or hierarchies?, ?a
body of conceptualizations?, ?schemata?, or ?metadata scheme?, ?a common set
of terms?, ?a controlled vocabulary of terms?, ?a representation vocabulary?,
or ?a body of knowledge?, etc.
Being puzzled by such a semantic ambiguity, the SW researchers and AI
developers start progressively wondering whether the
whole activity of ontology is about:
the
inquiry of reality, its forms, kinds and properties;
or
just about some general concepts with their formal logical relations;
or,
are they supposed to deal with ?the nominal? ontology of terms and their
semantic relationships.
As
a big result, we all, by bad or good luck involved with this great
mystifier, have to decide on
one of three perspectives, reality-centric, concept-driven, or name-based;
namely:
the
world ontology
of entities and their external
relationships? (realistic and veridical);
the
concept ontology
(conceptual and notional);
the
word ontology
(linguistic and nominal).
And the stakes are rather high. The way we are going to choose might
somehow affect the validity of the whole cause of Knowledge Technology, as
well as the semantic web initiative.
with good wishes to all,
Azamat Abdoullaev
Did I get your attention?
:-)
Actually, I'm only half
kidding. I am relieved that we are not giving the "definition" of
ontology the status as the subject of an entire conference! That would,
once and for all, cement the prejudice against ontology enthusiasts as stuffy
academics breathing their own exhaust. Let's get practical for a
minute.
Folks, there are word
senses. Ontology in the philosophic sense has meant one thing (and
generated plenty of fumes on its own). Ontology in the AI world meant
another thing, or three, depending on who you asked. But 15 years ago
there was a public effort by the AI community to enable knowledge sharing and
an explicit notion of ontology as a mechanism for exposing a kind of interface
at the semantic level for these AI systems (Neches and company, 1991). I
attempted to clarify that definition in what has become a highly cited
reference on the web and in print. Today we have an even more public and
visible effort in the Semantic Web, which uses the word ontology in the same
sense. There are tools (Protégé), Standard languages (OWL), etc for
ontology in *this sense*. We, computer scientists, have a sense of the
word ontology just like we have word senses for process, job, client, server,
and so forth.
What matters to me, and folks who
want to build real things with lots of real folks using them, is what
ontologies (in this sense) are good for.
I have noodled on this issue a
bit, and here is my minimal set of Things You Can Do With An
Ontology:
- specify a shared
conceptualization to enable knowledge sharing among systems (surprised?).
For example, clarifying *a* way to model engineering equations that
enables model sharing across web services. Or modeling the data in
disparate geospatial or life science data sources.
- specify a conceptualization in a
formalism so that others can reuse the IP or theoretical contribution of that
conceptualization. This is like specifying an algorithm in a pseduocode
that can be trivially translated into real programming languages, or
describing the chemical formula for a molecule in a report on its
pharmacological effects. For some AI system builders, the IP could be a
Domain Ontology or Upper Ontology; in this case, it serves the same function
as what mainstream software engineering calls Application Frameworks -- you
extend and instantiate the framework to build a new
application.
- represent a body of knowledge
about the meanings of words or other symbols. This is a tricky one to
minimize, but I still think it is only a single case. You can use
knowledge about the meanings of words for lots of applications: natural
language understanding, natural language generation, providing a UI for
browsing a large collection of texts, disambiguating words in a text search
engine, building entity extractors for structure elicitation from text,
biasing machine learning algorithms, etc.
OK, so should we have three
definitions and counting? I don't think it is necessary. If we
take conceptualization as not controversial (even to semiotics), then we can
just say that ontologies (for computer-based information) are a specification
of a conceptualization. Specifications are always a mix of formal and informal
content; the form isn't the thing. If we start here we can get on to the
important business of talking about what kinds of specifications (or
formalisms, if that is your medium) are better than others for which purposes.
For knowledge sharing, the specification can be evaluated on the basis
of its properties as an interface (minimality, context independence, etc).
For encoding knowledge for reuse, one could apply the same
criteria with the added advantage of lots of real examples where a concept in
a real theory needs to be said and a specification formalism can or cannot say
it.
For representing linguistic
knowledge, the criteria are harder to make objective, and I think this is the
source of a lot of heat on this list. The conceptualization for case
three has domain content such as assertions about which concepts are
specializations of other concepts. So the value of an ontology for NL
seems to be tied up with how well it reflects language use in a culture and
not so much about its structural properties. In other words, it's about
the conceptualization and not the specification. And if my linguistics
knowledge is up to the current fashion, the meaning of language in a culture
is rather fluid and messy. The valor of such an ontology, it seems,
would be in the magic performed by the NL application, which is the usual
pragmatic cop-out for which I offer no alternative.
Conclusion? Let's talk about
ontology *use* and not ontology definitions. Let's talk about what
can be achieved, even what can be imagined, if ontologies were part of the
infrastructure in use by the real world.
Tom
Links:
http://www.aaai.org/Library/Magazine/vol12.php#Fall
http://www-ksl.stanford.edu/kst/what-is-an-ontology.html
http://tomgruber.org/writing/ontolingua-kaj-1993.htm
http://tomgruber.org/writing/onto-design.htm