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Jenny,
Your inclusion about "providing "support for multiple evolving ontologies for single domains" is a vital point. Richard L. Ballard has long contended that the race for knowledge superiority is all about knowledge dominance. That is, those companies that own most of the knowledge (publishers for example), who develop highly concentrated knowledgebase products, can and will dominate the language used in those fields. This is an economic reality since it is less costly to license a knowledgebase product from those who own them, rather than to develop them again.
This is made especially possible through the use of Ballard's Mark 3 theory-based semantic technology that allows users to fully integrate millions of concepts, ideas and thought patterns to the highest level of granularity (expected release Q1 2008 in Beta form). Every view point as posted on this board could be integrated into a knowledgebase - in fact, it would not be complete unless the insights and wisdom of the philosophers, logicians and others were not fully represented - as long as these valuable contributions have relevance to the knowledge domain being modeled.
The problem is that conventional technologies (to include W3C technologies), do not accommodate the issue of real time reification of multiple view points, new category formation, or category modification..... thus addressing the ontology merge issue. The different offerings being a layer cake of off-setting technologies versus a single, succinct, integration of human thought. From my opinion, as in other's, conventional technologies will continue to be thwarted by "layer cake workarounds" because there has been no viable solution to the complexity (too many moving parts) problem. How does the layer cake simulate imagination? aesthetics? attitudes? Why is it that software development tools limit the number of concept relationships beyond n-3. Where are the n-ary capacity software products that can scale to represent human thought? - real knowledge representation. The revolution is afoot, and those hold fast, who think they can have their cake and eat it to, will find a whole new world that has evolved well before them.
Another issue that has been sidestepped each time it has been brought up, is the issue of how self-consistent logic functions within a quantum world. How effective can it be?? Yes, at the macro level, self-consistency is essential (engineering, manufacturing, rules of the road, etc.), but at the micro level, there is a world that is ever changing and influenced by a non-logical mind - individual or collective. As the saying goes, "life will not be denied." I am wondering, and I am hopeful, this topic will be address by the learned leadership of this excellent forum.
Dennis L. Thomas Knowledge Foundations, Inc. Ofc (714) 890-5984 Cell (760) 500-9167 ------------------------------------------------ Managing the Complexity of Enterprise Knowledge On Aug 27, 2007, at 10:05 AM, Jenny ure wrote: Is interesting - similar to the use of 'concordancing' in language teaching - using mining of examples of words in context from multiple texts as a means of conveying current meaning in context. Caveats might be that -the same extraction process will provide different results over time - ie a snapshot of an evolving process -averaging out is only valid within the same population - across different populations it obscures real differences and contrary to inituitive assumptions , epidemiologists will tell you that this can introduce a misleading bias. Joseph Goguen had some nice caveats for seekers after 'a single unified ontology that attracts consensus because it "reflects the real underlying reality" , - which some might see this as providing.
He argued instead that if the need for conceptual diversity is accepted, it then follows that 'knowledge engineering should seek ways to support it, rather than ways to overcome, suppress, or subvert it' by providing ‘support for multiple evolving ontologies for single domains , accepting that translations among such theories will necessarily be partial and incomplete and providing tools to help construct such partial mappings'. Jenny Ure John F. Sowa wrote: Since this forum has been unusually quiet for the past week,
I thought I'd send a note to keep it from gathering dust.
Following is the abstract, URL, and an excerpt from an article
that presents an interesting method for deriving semantic
information from Google page counts.
John
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Source: http://homepages.cwi.nl/~paulv/papers/amdug.pdf
Automatic Meaning Discovery Using Google
Rudi Cilibrasi, CWI
Paul Vitanyi, CWI, University of Amsterdam,
National ICT of Australia
Abstract
We have found a method to automatically extract the meaning of words
and phrases from the world-wide-web using Google page counts. The
approach is novel in its unrestricted problem domain, simplicity
of implementation, and manifestly ontological underpinnings. The
world-wide-web is the largest database on earth, and the latent
semantic context information entered by millions of independent
users averages out to provide automatic meaning of useful quality.
We demonstrate positive correlations, evidencing an underlying
semantic structure, in both numerical symbol notations and
number-name words in a variety of natural languages and contexts.
Next, we demonstrate the ability to distinguish between colors
and numbers, and to distinguish between 17th century Dutch painters;
the ability to understand electrical terms, religious terms, and
emergency incidents; we conduct a massive experiment in understanding
WordNet categories; and finally we demonstrate the ability to do a
simple automatic English-Spanish translation.
[Excerpt from Section 1 of the above paper]
At the time of writing, Google searches 8,058,044,651 web pages.
Define the joint event xTy = {w : x,y 2 w} as the set of web pages
returned by Google, containing both the search term x and the search
term y. The joint probability p(x,y) = |{w : x,y 2 w}|/M is the
number of web pages in the joint event divided by the overall number
M of web pages possibly returned by Google. This notation also
allows us to define the probability p(x|y) of conditional events
x|y = (xTy)/y defined by p(x|y) = p(x,y)/p(y).
In the above example we have therefore p(horse) ~ 0.0058,
p(rider) ~ 0.0015, p(horse, rider) ~ 0.0003. We conclude that
the probability p(horse|rider) of “horse” accompanying “rider” is
~ 1/5 and the probability p(rider|horse) of “rider” accompanying
“horse” is ~ 1/19. The probabilities are asymmetric, and it is the
lesser probability that is the significant one.
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