[unrev-II] Sowa on Semantic Nets --Fwd: [xtm-wg] Re: an introduction to the BCNGroup beadgames

From: Jack Park (jackpark@thinkalong.com)
Date: Wed Jul 25 2001 - 09:10:16 PDT

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    Based on a post by John Sowa to the XTM working group in response to some
    assertions made by another individual. The upshot is this: a URL to a
    great paper on semantic nets.

    From: "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
    > 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
    >and compares them to the corresponding mechanisms used in linear

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