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Mills,
This is, no doubt, the goal of semantic technologists - to achieve machine representations of every form of human knowledge to include values and beliefs - the stuff that underlies human culture. This includes the capacity to reason across this knowledge to answer questions and to predict outcomes and consequences. I found it interesting that Paola and Stephen Williams mentioned in a previous discussion that "We now are increasingly bumping into the limitations of simple triples," stating that "quads" were appearing on the horizon, perhaps as the "next gen semantics?"
From what I understand, the problem has to do with the structural limitations of procedural system applications. I think Stephen Williams signal's his agreement with this when he brought up the "K-arity" concept ("The K-arity PKR effective structure of knowledge, where K={3-10}, seems to cover it.") Richard Ballard has long contended that general knowledge representation requires n2-n12 "n-ary" relationships, but that medical diagnosis and other complex situations may require hundreds of conditional relationships. As noted in your own 2007 report, a Physician must know 2,000,000 concepts to effectively practice medicine. It is not unreasonable to think that it might require a few hundred of these concepts to diagnose a non-specific internal medical problem.
Williams mentions several other requirements for a robust semantic system such as "statements versioning" or "timestamps," security levels, ownership, etc. In this regard, Ballard states that all knowledge can be represented when each concept includes metaphysical, physical and time (universal, occurrence, continent) representations. In Ballard's world, the upper most primitives are metaphysics, physical reality & time.
The problem of modeling culture (generally refers to patterns of human activity and the symbolic structures that give such activity significance), is that conventional software cannot scale to seamlessly integrate all the concepts, their relationships, and the theory behind these concepts to achieve the meaningful points of view that are required to faithfully represent cultural "patterns of thought," at any level of granularity. Perhaps with the work of Paola, Williams and others, such a system will become a reality.
In the meantime, we are still confronted with the complexity problem.
Dennis
Dennis L. Thomas Knowledge Foundations, Inc. On Sep 14, 2007, at 3:25 PM, Mills Davis wrote: Gary,
I think this paper aims to articulate knowledge modeling needs of scholars studying cultures and the history of ideas. Historicity cannot be an afterthought and must accommodate the notion that concepts and categories evolve, including category theorys.
The development of UMLS in the life sciences provides an example. This started as key words, grew to curated taxonomies, then to a synthesis across 80 or so vocabularies and ontologies in a megathesaurus. Over time, it was found that concepts e.g. of a disease, may persist through time and across different terminology, but also the concept as well as the names by which it is known may evolve through time and further research. So, they changed their practices and how they modeled things.
Much of the history of IT has been preoccupied with record keeping and current accounts. Data has been shoved into box cars called fields, and retrieved from them and manipulated algorithmically with fixed logic. That is, system knowledge is fixed at design time and doesn't change, until the next version of the system is published, with the next version of knowledge encoded into it. During operation IT systems haven't learned, they just follow rote procedures.
Historically, there are many good reasons for the information and algorithm centric approach. The study of culture, however, calls for a richer palette both for knowledge representation, and for reasoning processes that encompass different axiologies, epistemologies, and research methodologies.
Description logic plus some overarching notions about logic unification (at the FOL level, I believe) is about where we are with the semantic web. We can expose data. But, todays semantic web standards do not provide an adequate foundation for the sorts of cultural researches, and knowledge-based computing that this author and other scholars envision, and are engaged in already.
Mills
On Sep 14, 2007, at 4:18 PM, Gary Berg-Cross wrote: On the paper by Veitman, or at least the summary there are enough vagaries to inspire a debate equal to that of the John Cage silence/hole. Taking “A semantic web in the cultural domain must enable us to trace how meaning and knowledge organisation have evolved historically in different cultures. “seems like an a vague requirement, for example especially when casting aside the challenges to define meaning as previously discussed. Gary Berg-Cross, Ph.D.
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703-742-0585 Here is a link to a paper by Kim Veltman: This paper argues that the computing community (and semantic web crowd in particular) has been overly fixated on first order logic and has failed thus far to produce a semantic web that is suitable for culture. The abstract reads: Today’s semantic web deals with meaning in a very restricted sense and offers static solutions. This is adequate for many scientific, technical purposes and for business transactions requiring machine-to-machine communication, but does not answer the needs of culture. Science, technology and business are concerned primarily with the latest findings, the state of the art, i.e. the paradigm or dominant world-view of the day. In this context, history is considered non-essential because it deals with things that are out of By contrast, culture faces a much larger challenge, namely, to re-present changes in ways of knowing; changing meanings in different places at a given time (synchronically) and over time (diachronically). Culture is about both objects and the commentaries on them; about a cumulative body of knowledge; about collective memory and heritage. Here, history plays a central role and older does not mean less important or less relevant. Hence, a Leonardo painting that is 400 years old, or a Greek statue that is 2500 years old, typically have richer commentaries and are often more valuable than their contemporary equivalents. In this context, the science of meaning (semantics) is necessarily much more complex than semantic primitives. A semantic web in the cultural domain must enable us to trace how meaning and knowledge organisation have evolved historically in different This paper examines five issues to address this challenge: 1) different world-views (i.e. a shift from substance to function and from ontology to multiple ontologies); 2) develop- ments in definitions and meaning; 3) distinctions between words and concepts; 4) new classes of relations; and 5) dynamic models of knowledge organisation. These issues reveal that historical dimensions of cultural diversity in knowledge organisation are also central to classification of biological diversity. New ways are proposed of visualizing knowledge using a time/space horizon to distinguish between universals and particulars. It is suggested that new visualization methods make possible a history of questions as well as of answers, thus enabling dynamic access to cultural and historical dimensions of knowledge. Unlike earlier media, which were limited to recording factual dimensions of collective memory, digital media enable us to explore theories, ways of perceiving, ways of knowing; to enter into other mindsets and world-views and thus to attain novel insights aSome practical consequences are outlined. On Sep 9, 2007, at 9:06 AM, John F. Sowa wrote:
Might I remind people that this thread started with a discussion of how the pieces of the SemWeb are related to ontology. John Cage's compositions are worth some discussion, but perhaps the amount spent so far has strayed a bit off topic. _________________________________________________________________
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Mills Davis Managing Director Project10X 202-667-6400 202-255-6655 cel 1-800-713-8049 fax
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Dennis L. Thomas
Knowledge Foundations, Inc. Ofc (714) 890-5984 Cell (760) 500-9167 ------------------------------------------------ Managing the Complexity of Enterprise Knowledge
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