[xtm-wg] Re: an introduction to the BCNGroup beadgames

["John F. Sowa" <sowa@bestweb.net>]

I can't believe that anyone would make the following statement
without joking or being incredibly naive:

> " As you might have surmised from my ontology paper, I have
> a single uniform coding system for all concepts. In any given knowledge
> base, absolutely everything (Content and Tool components) is characterized
> by a (Concept Sub-Code) and (Instance Sub-Code) pair."

That statement is at the level of claiming that C is a uniform coding
language for all knowledge because "absolutely everything (content
and tool components) can be characterized by a pair of pointers
to a (concept sub-code) and (instance sub-code) pair."

In 1957, Silvio Ceccato used the IBM 650 (a computer with a rotating
drum for memory) to represent all relations by a pair of pointers
and a code for the type of relation.  Conveniently, the 650 had
a word length of 10 decimal digits, of which the first two were the
code, and the next 8 were two pairs of 4-digit pointers.  But we have
come a long way from that "uniform representation".

If anyone is interested in a survey of the kinds of things that have
been done with semantic networks over the past 40+ years, I would
recommend my article:

   http://www.jfsowa.com/pubs/semnetw.htm

This article is a draft that will eventually be published in the
forthcoming _Encyclopedia of Cognitive Science_:

   http://www.macmillanonline.net/Science/ecs.htm

Following is the opening section.

John Sowa
__________________________________________________________________________

                           Semantic Networks

                              John F. Sowa

A semantic network or net is a graphic notation for representing
knowledge in patterns of interconnected nodes and arcs.  Computer
implementations of semantic networks were first developed for artificial
intelligence and machine translation, but earlier versions have long
been used in philosophy, psychology, and linguistics.

What is common to all semantic networks is a declarative graphic
representation that can be used either to represent knowledge or to
support automated systems for reasoning about knowledge.  Some versions
are highly informal, but other versions are formally defined systems of
logic.  Following are six of the most common kinds of semantic networks,
each of which is discussed in detail in one section of this article.

 1. Definitional networks emphasize the subtype or is-a relation between
    a concept type and a newly defined subtype.  The resulting network,
    also called a generalization or subsumption hierarchy, supports the
    rule of inheritance for copying properties defined for a supertype
    to all of its subtypes.  Since definitions are true by definition,
    the information in these networks is often assumed to be necessarily
    true.

 2. Assertional networks are designed to assert propositions.  Unlike
    definitional networks, the information in an assertional network is
    assumed to be contingently true, unless it is explicitly marked with
    a modal operator.  Some assertional netwoks have been proposed as
    models of the conceptual structures underlying natural language
    semantics.

 3. Implicational networks use implication as the primary relation for
    connecting nodes.  They may be used to represent patterns of
beliefs,
    causality, or inferences.

 4. Executable networks include some mechanism, such as marker passing
    or attached procedures, which can perform inferences, pass messages,
    or search for patterns and associations.

 5. Learning networks build or extend their representations by acquiring
    knowledge from examples.  The new knowledge may change the old
    network by adding and deleting nodes and arcs or by modifying
    numerical values, called weights, associated with the nodes and
    arcs.

 6. Hybrid networks combine two or more of the previous techniques,
    either in a single network or in separate, but closely interacting
    networks.

Some of the networks have been explicitly designed to implement
hypotheses about human cognitive mechanisms, while others have been
designed primarily for computer efficiency.  Sometimes, computational
reasons may lead to the same conclusions as psychological evidence.  The
distinction between definitional and assertional networks, for example,
has a close parallel to Tulving's (1972) distinction between semantic
memory and episodic memory.

Network notations and linear notations are both capable of expressing
equivalent information, but certain representational mechanisms are
better suited to one form or the other.  Since the boundary lines are
vague, it is impossible to give necessary and sufficient conditions that
include all semantic networks while excluding other systems that are not
usually called semantic networks.  Section 7 of this article discusses
the syntactic mechanisms used to express information in network
notations
and compares them to the corresponding mechanisms used in linear
notations.

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