Leo, thanks for putting a list of dimensions together. As I suggested
earlier, it would be helpful to the discussion if we could drill down these
dimensions with examples of existing ontologies or ontologies just made up for
the discussion.
Working from application development angle, I look at relevant ontologies
mainly in two dimensions: (1) the application areas or domains an ontology is
design for; and (2) the level of granularity an ontology has. The main purpose
of surveying existing ontologies in such a way is for re-using of the existing
ontological classes and properties and for increasing the chance for
interoperability. And so, my hope here is that we can brainstorm on any
possible way that will make the task of comparing ontologies and their classes
and properties easier for application developers. More importantly, a clear
framework for ontology comparison will potentially help people produce new
ontologies that are easier to be reused and integrated.
The examples I'm looking at are dublin core, FOAF, vCard, FuGO, EXPO. The
questions are:
(1) how do we categorize them from application and granularity
dimensions?
(2) can we identify/develop a common framework that improve our
understanding of these different ontologies and thus increase reuse and
integration?
(2) what suggestions can we make to the owners of the ontologies that will
increase the re-usability and interoperability of their new versions of
ontologies?
Any new thoughts or comments?
AJ
Quoting "Obrst, Leo J." <lobrst@xxxxxxxxx>:
>
Folks,
>
> To begin to address the "typing" of ontology that the
Ontology Summit
> prospectively would like to address, I'm beginning
this new thread. I'd
> suggest using the subject line to clearly
distinguish any NEW topic, so
> that skimmers/samplers/surfers can focus
more meaningfully. Why?
> Because we really don't yet have
metadata-annotated or "semantically"
> threaded email -- except for
subject lines. For example, currently we
> have the topic "ontology as
logical theory?".
>
> I'm bringing forward my suggestion (message
dated Thursday, January 25,
> 2007 2:14 PM, under the Subject = Re:
[ontology-summit] Defining
> "ontology"), simply as a strawman, slightly
enhanced with a canonical
> dimension/feature name (e.g., Formality) and
a scale of values (e.g.,
> Informal=0, Formal=1). I don't know if this
will prove useful, but let
> us know your thoughts. Note that these are
not pejorative: the feature
> name and the values are relatively
arbitrarily chosen (though I have my
> own biases).
>
>
Please comment on/modify/trash these. Note if you strongly believe in
>
one definition or think this effort is not worthwhile or the
dimensions
> are mischaracterized, please let us all
know.
>
>
> Dimensions of Ontology Types:
>
> 1)
Formality: Informal (Formality = 0) vs. Formal (Formality = 1)
>
2) Expressivity: Expressivity of the semantic model (i.e., underlying
>
knowledge
> representation language or logic) [No scale determined
yet]
> 3) Concept-based: Term (Concept-based = 0) vs. concept (real
world
> referent)(Concept-based = 1)
> 4) Mathematical Ordering:
Mathematical ordering, structure, definition
> of the privileged
>
parent-child relation: [No scale determined yet]
> 5) Application
focus/use cases, etc. (part of this is precision of the
> service
needed, e.g., metadata/topic terms for a document to aid in
> broad doc
topic retrieval vs. a semantic service query, specfication,
> or
composition): [No scale determined yet]
> 6) Granularity
(precision, scope): [No scale determined yet]
> 7) Development
Philosophy: Empirical (bottom-up) [0] vs. Rationalist
> (top-down)
development [1]
> methodology (i.e., arbitrary folks add or annotate
terms/concepts vs. a
> rigorous ontology development) [No scale
determined yet; Some
> combination? Middle-out? But what does that
mean?]
> 8) Human-Coded: Human-coded [1] vs. machine-learned/generated
[0]
> 9) Automated reasoning (and complexity of that, i.e., one could
have
> transitive closure or subsumption down a subclass graph
vs.
> theorem-proving): [No scale determined yet]
> 10)
Descriptive vs. prescriptive (i.e., a commonsense or
>
conceptually-profligate ontology vs. an ontology that specifies that
>
this is the way the world is): [No scale determined yet]
>
...
>
> Other criteria perhaps address properties of the content,
i.e., Average
> density/bushiness: [Probably a real scale, once we
define density,
> bushiness], etc.
>
>
> Some
notes:
> 1. Expressivity, Mathematical Ordering, and Automated Reasoning
are
> probably related.
> 2. Application focus/use cases: this is
still nearly arbitrary, but I
> would like us to think about
recall/precision of the application/use
> case as perhaps a better scale
or paired scales, i.e., [Recall = 0,
> Precision = 0] vs. [Recall = 1,
Precision = 1], but then we need to
> define Recall and Precision. Or
there may be some normalized score that
> factors both Recall and
Precision into a composite real number. Then
> again, a better dimension
may be application complexity if that can be
> agreed on, i.e.,
Constant, Linear, Logorithmic, Exponential, ...
> NP-Complete, ... Of
course, if we go with this latter notion, then it
> is probably closely
related to (1) above, i.e., if a problem is
> characterizable as
NP-Complete, then there is probably a corresponding
> expressivity model
requirement. This gets into expressive complexity,
> i.e., the
correlation between the expressiveness of the language and
> complexity
of the computation.
>
> Thanks,
> Leo
>
_____________________________________________
> Dr. Leo Obrst
The MITRE Corporation, Information Semantics
>
lobrst@xxxxxxxxx Center for Innovative Computing &
Informatics
> Voice: 703-983-6770 7515 Colshire Drive, M/S H305
>
Fax: 703-983-1379 McLean, VA 22102-7508,
USA
>
>
>
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--
AJ Chen,
PhD
http://www.web2express.org
"Open Data on Semantic
Web"