By Robert M. Colomb
ISBN-10: 1852335505
ISBN-13: 9781852335502
Details areas The structure of Cyberspace is aimed toward scholars taking info administration as a minor of their direction in addition to those that deal with rfile collections yet who're now not expert librarians. the 1st a part of this publication seems at how clients locate records and the issues they've got the second one half discusses the way to deal with the data house utilizing numerous instruments similar to category and regulated vocabularies. It additionally explores the overall problems with publishing, together with felony concerns, in addition the most problems with growing and coping with documents. Supported by way of routines and dialogue questions on the finish of every bankruptcy, the publication comprises a few pattern assignments appropriate to be used with scholars of this topic. A thesaurus can be supplied to aid readers comprehend the specialized vocabulary and the main innovations within the layout and evaluation of knowledge areas.
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Additional resources for Information Spaces The Architecture of Cyberspace
Sample text
Now let us consider two documents that should be as far apart as possible, that is a nearness of 0%. The first is part of the quotation from Meagher from Chapter 2. Document 4: Although occasionally the identity of the criminal organization will be notorious, more often than not its existence is not known or is the subject of merely vague rumor. And the other is part of a nonsense poem by Edward Lear "The Owl and the Pussycat". Document 5: The owl and the pussycat went to sea in a beautiful pea-green boat.
Following this example through, a nearness-based ISAR would respond to Query 3 with Documents 1 and 3, since they both have 100% nearness to Query 3 (they have all the terms in Query 3, and the nearness is normalized by the number of words in the smaller document). Assume that Document 1 is judged by the user as relevant, and the ISAR is asked to retrieve documents near to Document 1. Document 1 has 21 words, Document 2 has 24, and Document 3 has 11. Document 1 has 14 words in common with Document 2 and 5 words in common with document 3.
There are many other, more elaborate, schemes for generating weightings. The biggest weakness in automatically generated descriptors is that they are limited to the terms that actually occur in the documents. The "Interactive TV ... " news article example discussed above is a good example of this weakness. , nor the only slightly more general term "datacasting", since they don't occur in the text. The speech by Meagher referred to in Chapter 2 does not contain the term "Costigan Royal Commission".