Here is a list of some 1800 semantic domains. Each domain
(after cursory examination) seems to be a rather simple common everyday
experience extracted from all the noise of the day around it. Querying WordNet
with each domain would likely return a cloud of words around it in vocabulary
space, which could possibly be used to fill in each domain.
The domain IDs amount to a table of contents, with indentation
according to the author's viewpoint. So it appears to be a taxonomy of *mostly*
independent domains.
What the document describes as a domain seems to me to be the *context*
mentioned in the SCOP documents. So a SCoP rendering of the domains in this
document would provide the trains of words which the SCoP method can use to
select the properties useful in each individual domain for that phrase, not
just for that word.
Suggestions, thoughts, comments appreciated.
Sincerely,
Rich Cooper,
Rich Cooper,
Chief Technology Officer,
MetaSemantics Corporation
MetaSemantics AT EnglishLogicKernel DOT com
( 9 4 9 ) 5 2 5-5 7 1 2
http://www.EnglishLogicKernel.com
From:
ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Rich Cooper
Sent: Sunday, September 20, 2015 1:47 PM
To: '[ontolog-forum] '
Subject: [ontolog-forum] How to combine concepts - The SCOP method for
representing concepts in a combinable way
Dear Ontologists,
Here is a paper on the State, Context, Property (SCOP) way of
representing concepts:
http://cogprints.org/4770/1/hvf1.pdf
The book Women, Fire and Dangerous Things made it
clear that many concepts obey prototypes, such as mother, which
can be the birth mother, a nurturing adoptive mother, a woman who carries
another's implanted fertile egg to term, a step mother, and various other
prototype concepts, all of which are contained in the conceptual space for mother.
Each different prototype of the concept can have a unique set of properties,
according to the SCOP viewpoint.
From the paper,
Our theory proposes the
structure of a SCOP to model a concept. SCOP consists of a set of relevant
states S, a set of relevant contexts M, and a set of relevant properties L.
It is this possibility of
“dynamic change” under the influence of a context within SCOP that allows us to
model the combination of concepts. When concepts combine, they mutually affect
how they function as a context for each other,
and hence provoke the type of
dynamical change of state that is a basic aspect of our theory.
Does anyone have experience with this method for concept
representation? Does it work well with normal English text sentences to
create the intended concept of a noun with a train of complicated modifiers?
Sincerely,
Rich Cooper,
Rich Cooper,
Chief Technology Officer,
MetaSemantics Corporation
MetaSemantics AT EnglishLogicKernel DOT com
( 9 4 9 ) 5 2 5-5 7 1 2
http://www.EnglishLogicKernel.com
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Thomas
Johnston
Sent: Saturday, September 19, 2015 9:18 AM
To: [ontolog-forum]
Subject: Re: [ontolog-forum] FW: FW: CfP 11/16/2015: Knowledge-Based AI
Track at 2016 FLAIRS
(This is my second and last
catch-up post.)
The research of yours that
you mention is right in my own field of interests. I would appreciate any url
links you have to that work.
Concerning the
"drift" of "semantic fields" that you refer to, the
question that interests me is "What causes the drift?" I think that
perhaps there are two things that account for it.
The second, derivative,
factor is statistical. Semantics in a language is derivative on, ultimately,
the state of each individual's semantic net at a point in time. Continuities in
those semantic net states over time and over small to large communities of
speakers are the necessary condition of the use of language to communicate.
Those statistical patterns are periodically formalized in both general and
special-purpose dictionaries, whose definitions are based on guesses that
lexicographers make about the statistical patterns in those states over person
and over time, and the aggregate state reached in a given language community at
the point in time the definitions are formulated.
The first, basic, factor is
the plasticity of the human brain. Over time, and under the influence of what
we read and of our verbal communications with others, we evolve neurally-based
dispositions to use, and to accept as valid, pairs of words/expressions.
At the weaker end of the
dispositional spectrum for a given pair of expressions, we have dispositions
that correspond to statements expressing supposedly factual regularities. These
are Kant's synthetic a posteriori statements, empirical generalizations open to
revision.
At the stronger end of the
dispositional spectrum, we have dispositions that correspond to statements expressing
accepted linguistic conventions. These are Kant's analytic a priori statements,
ones not open to revision (except by revising the semantic rules the statements
express).
This dispositional spectrum
is what accounts for the analytic/synthetic continuum, and what explains why,
as Quine demonstrated, there is no analytic/synthetic dichotomy (thus nailing
shut the coffin of logical positivism).
Which brings to mind an
example provided by A. J. Ayer. He used the statement "Loadstones attract
iron" as an example, and suggested, quite reasonably, that this statement
must have begun life as an empirical generalization, but ended up as an
analytic statement, reflected in the fact that we just wouldn't count anything
as a loadstone if it didn't attract iron.
In an example I discussed in
my dissertation, a semantic key to the debate between pro- and anti-abortion
advocates is whether or not a fertilized human egg is a human being. For
anti-abortionists (at least those of a religious persuasion), "A fertilized
human egg is a human being" is what Kripke would classify as an analytic a
posteriori statement -- analytic because no empirical evidence would be counted
by them as evidence against the statement, and a posteriori because it is about
"matter of fact".
Reverting to Ayer's example,
the semantic drift of the pair "loadstones" and "attract
iron" was driven to an increasingly strong disposition to disallow
counter-examples. Certainly this semantic drift ended up with the disposition
to disallow counter-examples first in the scientists of that day, then
eventually in the wider community of speakers. The drift, for this pair of
expressions, was driven by the increasing scarcity of statements purportedly
expressing counter-examples. Each person's semantic net evolved over time from
a state in which counter-example statements would be at least considered, to a
state in which they were no longer considered.
For each such person, over
time, patterns of linguistic usage strengthened the connection between the two terms
until the statements expressing those connections became "true by
definition". Across increasingly wider linguistic communities, the
statistical aggregation force led to lexicographic revisions of the relevant
dictionaries.
But this drift across the
dispositional strength spectrum is not itself free. "Loadstones" and
"attract iron" are each connected with a large number of other
potentially co-occurring expressions, some of which would pull (via the
dynamism of neural connectivity) against that particular drift of that
particular pair of expressions. And so I reach a pale wash of neural metaphor
over Quine's conceptual holism.
I'd like to know if you can
tie this informal description of the proto-theory I have been working on for
four decades to academic work you are familiar with.
Regardless, thanks for
already responding to my original message to you.
Leo, you asked me, earlier,
to clarify some of what I said above, explaining in more current terminology
what my interests are, and where they are situated in the corpus of current
work. I am busy reading your Geeraerts references right now, and hope, with
that, to be able to respond in a week or so.
I have a lot of catching-up
to do!
On Saturday, September 19, 2015 12:05
PM, Thomas Johnston <tmj44p@xxxxxxx> wrote:
OK Leo. Here's my re-post, to
the forum, of what I've posted earlier to you.
For others, the topic I'm
concerned with in these postings is lexical semantics -- the semantics of
sub-sentential expressions. Also, I have begun reprising some of my ancient
notes on this topic, and they may be difficult to follow because over three
decades of work in lexical semantics have taken place since I wrote them. And
so there may be terminology I use that needs clarification. Also, my current
research in this field is at a rudimentary level, and so there may be
developments that, once I am aware of them, will point out the error of my
ways.
Contributions from others in
this forum with interests in lexical semantics are most welcome.
And so, what I wrote earlier
was this:
Thank
you for the overview of distributional semantics which, in fact, I was unaware
of. Also for the references you provide.
The
first thing that comes to mind is that the lexical use patterns which these
statistical techniques will certainly reveal / have revealed need a theory, an
explanation. Recently, I have gone back to fairly extensive unpublished
material which I wrote in the 70's and 80's. Combining it with my current
research and notes, I have a corpus of work which I provisionally think might
form the basis of such a theory.
Currently, I'm trying to
figure out how much of Gardenfors' new book, The Geometry of Meaning, has
already anticipated my work on such a theory. Unless you have already concluded
(as I nearly have) that his conceptual spaces won't carry all the weight he
puts on them, you might find this book of his interesting.
I learned my compositionality
lessons from Jerry Fodor's extended development and defense of the Language of
Thought. But what still fascinates me is the psychological phenomenon of a
child's progression from pointing and naming to his construction of elementary
sentences. It seems to me to be one of the miracles/mysteries of human
intellectual achievement. I look forward to an ANN account of compositionality,
from which point of view Fodor's symbol-based LOT will be seen as an abstract
description of what neural networks do, and which will begin the process of
removing the mystery from this miracle.
But the semantic forces which
account for the statistical patterns discussed in your references are still
what interest me the most. I can get into the literature starting from those
references, of course; but if there is anything else you know of that is
specifically concerned with a theory of lexical meaning (and lexical meaning
change), please let me know.
(There is one more catch-up
post I will publish to the forum, and then we're all starting from the same
page.)
On Friday, September 18, 2015
7:50 PM, "Obrst, Leo J." <lobrst@xxxxxxxxx> wrote:
[I originally posted this
just to you, but we have agreed to share our exchange and invite others into
the discussion.]
As you undoubtedly know,
recently there’s has been the emergence of so-called “distributional semantics”
these days in computational linguistics/NLP. This is based on corpus
linguistics, i.e., large-scale statistical, but light-weight “knowledge-based”
or formal linguistic/semantic methods.
Distributional semantics:
words as “meaning” the contexts/collocations they can occur in, what I
consider kind of Witgenstein 2 (Investigations, not Tractatus) in nature.
Some folks are trying to
combine distributional semantics with more formal compositional semantics, the
latter of which has not focused primarily on lexical semantics, but rather on
the composition of words into higher forms and their semantic
interpretations, i.e., going back to Montague in the late 1960s.
E.g., see [1, 2]. Also, for a good overview of lexical theories,
see [3]. For a new type-based approach, see [4].
However, there are
potentially some useful emerging approaches in ontology research, i.e., quality
(or value) spaces, and so-called semantic reference spaces/ranges, especially
[5-6]. We are using this in our current clinical care healthcare ontology
research (forthcoming), which can combine quantitative and qualitative quality
value spaces, so that, e.g., nominal qualities (“named” qualities; think of
“low/medium/high X”) can be mapped into a quantitative range, though
imprecisely, given that you have some ordering on the regions. I am myself
thinking of something similar to this for so-called “semantic fields”, i.e.,
that one can begin to think of these spaces and their points/regions as
“drifting” over time.
[3] Geeraerts, Dirk. 2009.
Theories of Lexical Semantics. Oxford University Press.
[4] Asher, Nicholas.
2011. Lexical Meaning in Context: A Web of Words. Cambridge: Cambridge
University Press. 2011.
[6] Probst, F. 2008.
Observations, measurements and semantic reference spaces. Applied Ontology 3
(2008) 63-89.
The description of
knowledge-based vs "statistics"-based AI is an entry point into so
much that I am interested in. Perhaps the distinction will eventually cease to
be a distinction between the association of ontologies with the former but not with
the latter. Perhaps the distinction will eventually become one between software
systems which are given human-developed ontologies, and software systems which
abstract their own ontologies from the patterns they discover by naming those
patterns and then developing additional patterns by playing with set-theoretic
quasi-random combinations of those named patterns.
And the way we play these
set-theoretic games in our heads, I have suspected for a long time, is by means
of background processes in which Venn-diagram-like representations of those
labelled patterns are what is actually manipulated, at close to the neural
level. (This is a bit like Gardenfors, but also quite a bit unlike him.)
I suspect this is far too
brief, and also far too off the beaten academic path, to be more than vaguely
suggestive, if even that. The interests that it hints at are (i) my interest in
lexical semantics, vs. what I see as a one-sided concentration on the semantics
of statements on the part of philosophers especially; and (ii) my interest in
diachronic semantics, i.e. in how the semantic web that is embodied in the
neurochemical web of our brains evolves over time, as it obviously does.
I'm tempted to just erase
this. But I feel among friends here, and so I've decided that it doesn't matter
if this sounds foolish. And if there are others here to whom this is not
completely non-sensical, I'd enjoy hearing from you, and especially hearing
about the "core bibliographies" relevant to this stuff that you are
currently working with.
On Tuesday, September 15,
2015 12:27 AM, "Obrst, Leo J." <lobrst@xxxxxxxxx> wrote:
-----Original Message-----
FLAIRS Knowledge-Based AI Track
Special Track at FLAIRS-29, Key Largo, Florida USA
In cooperation with the Association for the Advancement of
Artificial
Paper submission deadline: November 16, 2015.
Notifications: January 18, 2016.
Camera-ready version due: February 22, 2016.
All accepted papers will be published as FLAIRS proceedings by the
AAAI.
What is Knowledge-Based AI?
After an early dominance in AI, especially in NLP, approaches
based on
engineered knowledge-resources such as rule bases and ontologies
modeling
(part of) the world, the statistical winter set in in the 1980s. Fueled
by
increased computing power, statistics-based replacements for
modeling the
world, including machine-learning and neural networks, lead to
early
successes before they hit their ceiling and resulted in
algorithmic arms
races. To get AI out of these trenches and back into mobile
warfare,
knowledge-based methods have not only been being paid
lipservice to in
the many "semantic" revolutions, but actual applications
have been built,
often with complementary methodologies that paired statistical and
knowledge-based solutions.
The scope of the track includes research, proof-of-concept and
industry
applications in the area of knowledge-based AI, i.e. systems whose
functionality is informed by computational knowledge resources
(ontologies, lexicons, semantic networks and/or knowledge bases).
While
the knowledge-based AI is often juxtaposed to the statistics-based
AI, we
see the contrast as unnecessarily exclusionary, in that systems
combining
the intuitive directness of knowledge representation with the
efficiency
of statistics-based computation have distinct advantages.
What is the GOAL of the track?
To showcase recent knowledge-based theories, methodologies, and
applications in AI and to foster new approaches of this kind, also
those
paired with statistical and machine-learning approaches.
The scope of the track includes research, proof-of-concept and
industry
applications in the area of knowledge-based AI, i.e. systems whose
functionality is informed by computational knowledge resources
(ontologies, lexicons, semantic networks and/or knowledge bases).
While
the knowledge-based AI is often juxtaposed to the statistics-based
AI, we
see the contrast as unnecessarily exclusionary, in that systems
combining
the intuitive directness of knowledge representation with the
efficiency
of statistical approximation have distinct advantages.
What kind of studies will be of interest?
Papers and contributions are encouraged for any work relating to
Knowledge-Based AI. Topics of interest may include (but are in no
way
€ spreading activation networks
€ applications in knowledge-based AI
€ hybrid probabilistic/machine-learning & knowledge-based systems
Note: We invite original papers (i.e. work not previously
submitted, in
submission, or to be submitted to another conference during the
reviewing
Interested authors should format their papers according to AAAI
formatting
guidelines. The papers should be original work (i.e., not
submitted, in
submission, or submitted to another conference while in review).
Papers
should not exceed 6 pages (4 pages for a poster) and are due by
November
16, 2015. For FLAIRS-29, the 2016 conference, the reviewing is a
double
blind process. Fake author names and affiliations must be used on
submitted papers to provide double-blind reviewing. Papers must be
submittcoued as PDF through the EasyChair conference system, which
can be
accessed through the main conference web site
EasyChair login - your EasyChair account information is hidden
from
reviewers. Authors should indicate the [your track name] special
track for
submissions. The proceedings of FLAIRS will be published by the
AAAI.
Authors of accepted papers will be required to sign a form
transferring
copyright of their contribution to AAAI. FLAIRS requires that
there be at
least one full author registration per paper.
Papers will be refereed and all accepted papers will appear in the
conference proceedings, which will be published by AAAI Press.
€ Christian F. Hempelmann, Texas A&M University-Commerce,
€ Gavin Matthews, NTENT.com,
€ Max Petrenko, NTENT.com,
€ Christian F. Hempelmann, Texas A&M University-Commerce,
€ Elena Kozerenko, Russian Academy of Sciences,
€ Gavin Matthews, NTENT.com,
€ Max Petrenko, NTENT.com,
€ Victor Raskin, Purdue University,
€ Julia M. Taylor, Purdue University,
€ Tony Veale, University College Dublin,
€ Yorick Wilks, University of Sheffield & IHMC, Florida,
€ Michael Witbrock, VP for Research, Cycorp.
Questions regarding the Knowledge-Based AI Special Track should be
addressed to the track co-chairs:
€ Christian F. Hempelmann, Texas A&M University-Commerce,
Questions regarding Special Tracks should be addressed to Zdravko
Markov,
Conference Chair: William (Bill) Eberle, Tennessee Technological
Program Co-Chairs: Zdravko Markov, Central Connecticut State
University,
Special Tracks Coordinator: Vasile Rus, The University of Memphis,
USA
Paper submission site: follow the link for submissions at
Christian F. Hempelmann, PhD | Assistant Professor of
Computational
Department of Literature and Languages
Texas A&M University-Commerce
P.O. Box 3011 | Commerce, TX 75429-3011
The Texas A&M University System
_______________________________________________
Dr. Leo
Obrst
The MITRE Corporation, Information Semantics
Voice: 703-983-6770
7515 Colshire Drive, M/S H317
Fax:
703-983-1379 McLean, VA 22102-7508, USA
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