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Source

Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas

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Abstract

Among the theorists in the field of subject analysis in the twentieth century, none has been more influential than S. R. Ranganathan (1892-1972) of India, a mathematician by training who turned to librarianship and made some of the most far-reaching contributions to the theory of librarianship in general and subject analysis in particular. Dissatisfied with both the Dewey Decimal Classification and the Universal Decimal Classification, Ranganathan set out to develop his own system. His Colon Classification was first published in 1933 and went through six editions; the seventh edition was in progress when Ranganathan died in 1972. In the course of developing the Colon Classification, Ranganathan formulated a body of classification theory which was published in numerous writings, of which the best known are Elements of Library Classification (1945; 3rd ed., 1962) and Prolegomena to Library Classification (1967). Among the principles Ranganathan established, the most powerful and influential are those relating to facet analysis. Ranganathan demonstrated that facet analysis (breaking down subjects into their component parts) and synthesis (recombining these parts to fit the documents) provide the most viable approach to representing the contents of documents. Although the idea and use of facets, though not always called by that name, have been present for a long time (for instance, in the Dewey Decimal Classification and Charles A. Cutter's Expansive Classification), Ranganathan was the person who systematized the ideas and established principles for them. For his Colon Classification, Ranganathan identified five fundamental categories: Personality (P), Material (M), Energy (E), Space (S) and Time (T) and the citation order PMEST based an the idea of decreasing concreteness.

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Abstract

The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.

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Abstract

The selection in this reader by R. M. Needham and K. Sparck Jones reports an early approach to automatic classification that was taken in England. The following selection reviews various approaches that were being pursued in the United States at about the same time. It then discusses a particular approach initiated in the early 1960s by Harold Borko, at that time Head of the Language Processing and Retrieval Research Staff at the System Development Corporation, Santa Monica, California and, since 1966, a member of the faculty at the Graduate School of Library and Information Science, University of California, Los Angeles. As was described earlier, there are two steps in automatic classification, the first being to identify pairs of terms that are similar by virtue of co-occurring as index terms in the same documents, and the second being to form equivalence classes of intersubstitutable terms. To compute similarities, Borko and his associates used a standard correlation formula; to derive classification categories, where Needham and Sparck Jones used clumping, the Borko team used the statistical technique of factor analysis. The fact that documents can be classified automatically, and in any number of ways, is worthy of passing notice. Worthy of serious attention would be a demonstra tion that a computer-based classification system was effective in the organization and retrieval of documents. One reason for the inclusion of the following selection in the reader is that it addresses the question of evaluation. To evaluate the effectiveness of their automatically derived classification, Borko and his team asked three questions. The first was Is the classification reliable? in other words, could the categories derived from one sample of texts be used to classify other texts? Reliability was assessed by a case-study comparison of the classes derived from three different samples of abstracts. The notso-surprising conclusion reached was that automatically derived classes were reliable only to the extent that the sample from which they were derived was representative of the total document collection. The second evaluation question asked whether the classification was reasonable, in the sense of adequately describing the content of the document collection. The answer was sought by comparing the automatically derived categories with categories in a related classification system that was manually constructed. Here the conclusion was that the automatic method yielded categories that fairly accurately reflected the major area of interest in the sample collection of texts; however, since there were only eleven such categories and they were quite broad, they could not be regarded as suitable for use in a university or any large general library. The third evaluation question asked whether automatic classification was accurate, in the sense of producing results similar to those obtainabie by human cIassifiers. When using human classification as a criterion, automatic classification was found to be 50 percent accurate.

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Abstract

This illustrates a stark reality of the Web. There are many resources for environmental content, but there is no central repository of authoritative information that meets the needs of diverse user communities. The Encyclopedia of Earth aims to fill that niche by providing content that is both free and reliable. Still in its infancy, the EoE already is an integral part of the emerging effort to increase free and open access to trusted information on the Web. It is a trusted content source for authoritative indexes such as the Online Access to Research in the Environment Initiative, the Health InterNetwork Access to Research Initiative, the Open Education Resources Commons, Scirus, DLESE, WiserEarth, among others. Our initial Content Partners include the American Institute of Physics, the University of California Museum of Paleontology, TeacherServe®, the U.S. Geological Survey, the International Arctic Science Committee, the World Wildlife Fund, Conservation International, the Biodiversity Institute of Ontario, and the United Nations Environment Programme, to name just a few. The full partner list here can be found at <http://www.eoearth.org/article/Content_Partners>. We have a diversity of article types including standard subject articles, biographies, place-based entries, country profiles, and environmental classics. We recently launched our E-Book series, full-text, fully searchable books with internal hyperlinks to EoE articles. The eBooks include new releases by distinguished scholars as well as classics such as Walden and On the Origin of Species. Because history can be an important guide to the future, we have added an Environmental Classics section that includes such historical works as Energy from Fossil Fuels by M. King Hubbert and Undersea by Rachel Carson. Our services and features will soon be expanded. The EoE will soon be available in different languages giving a wider range of users access, users will be able to search it geographically or by a well-defined, expert created taxonomy, and teachers will be able to use the EoE to create unique curriculum for their courses.

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Footnote

Bezugnahme auf: Gardner, M.: Is mathematics "out there"? In: The Mathematical intelligencer 23(2000) no.1, S.7-8