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" <email@example.com>
>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:
>This article is a draft that will eventually be published in the
>forthcoming _Encyclopedia of Cognitive Science_:
>Following is the opening section.
> 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
> 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
> 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
> 6. Hybrid networks combine two or more of the previous techniques,
> either in a single network or in separate, but closely interacting
>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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This archive was generated by hypermail 2b29 : Wed Jul 25 2001 - 09:25:12 PDT