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Date

8. 7.2000 18:17:22

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Date

3.10.2000 10:22:59

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Abstract

A medium-length book, based on lectures, aimed at classificationists, not cataloguers or classifiers. Ranganathan begins with his Five Laws and a definition of classification and its purposes. He gives a list of 108 subjects in "unhelpful alphabetical sequence" and shows how they can be grouped into subjects, and then how each each subject's terms can be organized in a helpful and useful way, thereby demonstrating and building up his basic canons, postulates, and principles of classification. All of that, roughly the first half of the book, will be of interest to anyone starting to make classification systems. It has all of what makes Ranganathan's work so delightful to read: his unending concern for the user, his deep thought, and his warmth, humour, and spirituality. The second half of the book, however, has what can make his work difficult: his unyielding belief that the Colon Classification is the only system worth using. The reader will not be very interested in repeatedly classifying books under various systems and then reversing the process to see how closely the call number matches the subject. However, the reader can take the ideas so clearly presented in the first half of the book and then veer off to build his or her own system, while remembering that if his classification laws are likened to Euclid's laws of geometry, there is no room for a Riemann or Lobachevksy in Ranganathan's strict world.

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Abstract

In distinction to current Theory of Communication which treats amount of information as a measure of the statistical rarity of a message, a Theory of Semantic Information is outlined, in which the concept of information carried by a sentence within a given language system is treated as synonymous with the content of this sentence, normalized in a certain way, and the concept of amount of semantic information is explicated by various measures of this content, all based on logical probability functions ranging over the contents. Absolute and relative measures are distinguished, so are D-functions suitable for contexts where deductive reasoning alone is relevant and I-functions suitable for contexts where inductive reasoning is adequate. Of the two major types of amount of information investigated, the one, cont, is additive with respect to sentences whose contents are exclusive, the other, inf, with respect to sentences which are inductively independent. The latter turns out to be formally analogous to the customary information measure function. Various estimate functions of amount of information are investigated leading to generalized semantic correlates of concepts and theorems of current Communication Theory. A concept of semantic noise is tentatively defined, so are efficiency and redundancy of the conceptual framework of a language system. It is suggested that semantic information is a concept more readily applicable to psychological and other investigations than its communicational counterpart.