Hi John;
Reconciling upper ontologies is certainly
not for the faint of heart. I was not a trivial task to reconcile and
combine the event models of HPKB Cyc and IODE. But once I studied and
deeply understood each model, I was able to make a small number of fixes to
each artifact. The models then slid together with a few taps of the
mallet. OK, it was actually a rather iterative process ;^)
I find that the act of reconcilations is
the ultimate test for correctness of the respective artifacts.
I should mention that I did use some
immuno-supressive drugs on Cyc. I didn't have time to worry about its
meta-types so I temporarily ripped them out. So my merged artifact has the
Cyc collections (classes) that I used as being all instances of some rather
general IODE class (metaclass). I certainly
believe in the logical correctness of meta-types and hyper-meta-types (turtles
all the way down), I just don't find them that useful for my present
tasks.
So my merging phylosophy
involves starting by merging the parts that one really needs and "fixing"
the nits in both artifacts that prevent merging. One of the "fixes" was
actually not a fix but a change of a temporal definition of one to agree with
the other. I made time points intervals in the IODE definitions.
IODE had a perfectly reasonable defintion, but I would have to change much more
in Cyc to get it to agree with IODE. Ontlogy Works has been very
cooperative and supportive of the effort.
Note, please, that I am not merging to
prove that we can all join hands and merge. I simply need many more
definitions than I presenly have and I find it very cost effective to grab
definitions from Cyc. I occasionally need to fix them. This upper
ontology allignment is simply a means of making it easier to import Cyc content
into IODE.
Best,
-Eric Peterson
From: ontac-forum-bounces@xxxxxxxxxxxxxx on
behalf of John F. Sowa
Sent: Sat 5/13/2006 11:07 PM
To:
ontac-forum@xxxxxxxxxxxxxx
Subject: [ontac-forum] Problems of
ontology
Folks,
I recently returned from the FLAIRS-06 conference,
where Alan Bundy
gave a talk that supports many of the points I have been
trying to
make in various discussions: A single, unified upper ontology
is
impossible to achieve, and it's not necessary for
interoperability.
As an example of a contrary view, I'll quote some old
email from
March 2:
Mike Uschold:
> The issue of
import is whether we can agree on some variation of:
>
>
"A common upper ontology is essential for achieving affordable and
>
scalable semantic interoperability. Summit participants will
explore
> alternative approaches to developing or establishing this
common upper
> ontology."
>
> My original
comment was: I cannot endorse this statement for two
>
reasons.
>
> 1. I don't know that it is
'essential'.
> 2. I don't believe is possible to have a single
CUO.
Nicola Guarino:
> I agree, although, if it's pretty
clear that an ULO is not essential
> for semantic interoperability,
it *seems* indeed essential for
> *affordable and scalable* semantic
interoperability. The latter is
> hard to prove, however.
I
think the reason why it's hard to prove is that it's false.
People have
been building interoperable computer systems for the
past 50 years (and for
many centuries before that, people have
been using interoperable systems for
banking, plumbing, railroads,
telegraph, electrical transmission,
etc.). None of them have ever
had agreement on anything more than the
local task-oriented aspects
-- i.e., the details of the message formats, the
signals that are
transmitted, or the size and shape of the connectors (e.g.,
the
plumbing or the railroad track).
Following is the abstract and
introduction of Bundy's paper.
John
Sowa
____________________________________________________________________
On
Repairing Reasoning Reversals via Representational Refinements
Alan
Bundy, Fiona McNeill and Chris Walton
Abstract
Representation is a
fluent. A mismatch between the real
world and an agent's representation
of it can be signalled by
unexpected failures (or successes) of the agent's
reasoning.
The `real world' may include the ontologies of other
agents.
Such mismatches can be repaired by refining or abstracting
an
agent's ontology. These refinements or abstractions may
not be limited to
changes of belief, but may also change the
signature of the agent's ontology.
We describe the implementation
and successful evaluation of these ideas in
the ORS system.
ORS diagnoses failures in plan execution and then repairs
the
faulty ontologies. Our automated approach to dynamic ontology
repair
has been designed specifically to address real issues
in multi-agent systems,
for instance, as envisaged in the
Semantic
Web.
Introduction
The first author [AB] has a vivid memory of his
introductory
applied mathematics lecture during his first year at
university.
The lecturer delivered a sermon designed to rid the
incoming
students of a heresy. This heresy was to entertain a
vision of a
complete mathematical model of the world. The
lecturer correctly prophesied
that the students were dissatis-
fied with the patent inadequacies of the
mathematical models
they had learnt at school and impatient, now they had
arrived
in the adult university world, to learn about sophisticated
models
that were free of caveats such as treating the weight
of the string as
negligible or ignoring the friction of
the pulley
They were to be
disappointed. Complete mathematical
models of the real world were
unattainable, because it was
infinitely rich. Deciding which elements of the
world were
to be modelled and which could be safely ignored was the
very
essence of applied mathematics. It was a skill that
students had to learn ?--
not one that they could render
redundant by modelling everything.
This
all now seems obvious. AB is surprised at the naivety
of his younger self ?
since, before the sermon, he certainly
was guilty of this very heresy. But it
seems this lesson
needs to be constantly relearnt by the AI community. We
too
model the real world, for instance, with symbolic representa-
tions of
common-sense knowledge. We too become impatient
with the inadequacies of our
models and strive to enrich
them. We too dream of a complete model of
common-sense
knowledge and even aim to implement such a model,
cf. the Cyc
Project. But even Cycorp is learning to cure
itself of this heresy, by
tailoring particular knowledge bases
to particular applications, underpinned
by a common core.
If we accept the need to free ourselves of this heresy
and
accept that knowledge bases only need to be good enough
for their
application, then there is a corollary that we must
also accept: the need for
the knowledge in them to be fluent,
i.e., to change during its use. And, of
course, we do accept
this corollary. We build adaptive systems that learn to
tailor
their behaviour to a user or improve their capabilities over
time.
We have belief-revision mechanisms, such as truth
maintenance (Doyle 1979),
that add and remove knowledge
from the knowledge base.
However, it is
the thesis of this paper that none of this goes
far enough. In addition, we
must consider the dynamic evolution
of the underlying formalism in which the
knowledge is represented.
To be concrete, in a logic-based representation the
predicates
and functions, their arities and their types, may all need
to
change during the course of reasoning.
Once you start looking,
human common-sense reasoning
is full of examples of this requirement.
Consider, for
instance, Joseph Black's discovery of latent heat.
Before
Black, the concepts of heat and temperature were conflated.
It was
thus a paradox that a liquid could change heat content,
but not temperature,
as it converted to a solid or a gas.
Before formulating his theory of latent
heat, Black had to
separate these two conflated concepts to remove the
paradox
(Wiser & Carey 1983). Representational repair can also
move in
the opposite direction: the conation of ?morning
star? and ?evening star?
into ?Venus?, being one of the most
famous examples.
But such
representational refinement is not a rare event
reserved to highly creative
individuals; it's a commonplace
occurrence for all of us. Everyday we form
new models to
describe current situations and solve new problems:
from
making travel plans to understanding relationships with and
between
newly met people. These models undergo constant
renement as we learn more
about the situations and get
deeper into the problems.
Consider, for
instance, the commonplace experience of
buying something from a
coin-in-the-slot machine. Suppose
the item to be bought costs £2. Initially,
we may believe that
having £2 in cash is a sufficient precondition for the
buying
action. However, we soon learn to refine that precondition
to
having £2 in coins --? the machine does not take notes.
When we try to use
the coins we have, we must refine further
to exclude the new 50p coins --?
the machine is old and has
not yet been updated to the new coin. But even
some of the,
apparently legitimate, coins we have are rejected.
Perhaps
they are too worn to be recognised by the machine. Later a
friend
shows us that this machine will also accept some foreign
coins, which,
apparently, it confuses with British ones.
Rening our preconditions to adapt
them to the real world
of this machine does not just involve a change of
belief.
We have to represent new concepts: ?coins excluding the new
50p?,
?coins that are not too worn to be accepted by this
particular machine?,
?foreign coins that will fool this machine?,
etc.
As another example,
consider the experiment conducted by
Andreas diSessa on first-year MIT
physics students (diSessa 1983).
The students were asked to imagine a
situation in which a ball
is dropped from a height onto the floor.
Initially, the ball
has potential but not kinetic energy. Just before
it hits the
floor it has kinetic but not potential energy. As it hits
the
floor it has neither. Where did the energy go? The
students
had trouble answering this question because they had
idealised
the ball as a particle with mass but no extent. To solve
the
problem they had to refine their representation to give the
ball
extent, so that the `missing' energy could be stored in
the
deformation of the ball. Note that this requires a change in
the
representation of the ball, not just a change of belief
about it.
The
investigation of representational refinement has become
especially urgent due
to the demand for autonomous,
interacting software agents, such as is
envisaged in the
SemanticWeb (Berners-Lee, Hendler, & Lassila 2001). To
enable
such interaction it is assumed that the agents will share a
common
ontology. However, any experienced programmer
knows that perfect ontological
agreement between very large
numbers of independently developed agents is
unattainable.
Even if all the ontology developers download their
ontologies
from the same server, they will do so at different times
and
get slightly different versions of the ontology. They will
then tweak the
initial ontologies to make them better suited
to their particular
application. We might safely assume a
~90% agreement between any two agents,
but there will always
be that ~10% disagreement and it will be a different
10% for
each pair. The technology we discuss below provides a
partial
solution to just this problem.
Note that our proposal
contrasts with previous approaches
to ontology mapping, merging or aligning.
Our mechanism
does not assume complete access to all the ontologies
whose
mismatches are to be resolved. Indeed, we argue that
complete access will
often be unattainable for commericial
or technical reasons, e.g., because the
ontologies are being
generated dynamically. Moreover, our mechanism
doesn't
require an off-line alignment of these mismatching
ontologies.
Rather, it tries to resolve the mismatches in a
piecemeal
fashion, as they arise and with limited, run-time
interaction
between the ontology owning agents. It patches the
ontologies
only as much as is required to allow the agent interaction
to
continue successfully. Our mechanism is aimed at ontologies
that are largely
in agreement, e.g., different versions of
the same ontology, rather than
aligning ontologies with a
completely different pedigree, which is the normal
aim of
conventional ontology mapping. It works completely
automatically.
This is essential to enable interacting agents
to resolve their ontological
discrepancies during run-time
interactions. Again, this contrasts with
conventional ontology
mapping, which often requires human
interaction.
_________________________________________________________________
Message
Archives: http://colab.cim3.net/forum/ontac-forum/
To
Post: mailto:ontac-forum@xxxxxxxxxxxxxx
Subscribe/Unsubscribe/Config:
http://colab.cim3.net/mailman/listinfo/ontac-forum/
Shared
Files: http://colab.cim3.net/file/work/SICoP/ontac/
Community
Wiki: http://colab.cim3.net/cgi-bin/wiki.pl?SICoP/OntologyTaxonomyCoordinatingWG
_________________________________________________________________
Message Archives: http://colab.cim3.net/forum/ontac-forum/
To Post: mailto:ontac-forum@xxxxxxxxxxxxxx
Subscribe/Unsubscribe/Config:
http://colab.cim3.net/mailman/listinfo/ontac-forum/
Shared Files: http://colab.cim3.net/file/work/SICoP/ontac/
Community Wiki:
http://colab.cim3.net/cgi-bin/wiki.pl?SICoP/OntologyTaxonomyCoordinatingWG (01)
|