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Abstract

Attempts to automate classification have focused on mimicking the intellectual processes whereby human classifiers assign entities to mutually exclusive groups that exhibit or more shared characteristics. A more viable approach might be to construct an adaptive retrieval system that produces groupings of related entities by generating dynamic categories based on document content and on the system's emergent structure as it adapts to modifications in the database and to observed patterns of access. Presents a theoretical model for adaptive information networks using relevance feedback and genetic algorithms to generate emergent structure

Imprint

Alberta : Alberta University, School of Library and Information Studies

Source

Connectedness: information, systems, people, organizations. Proceedings of CAIS/ACSI 95, the proceedings of the 23rd Annual Conference of the Canadian Association for Information Science. Ed. by Hope A. Olson and Denis B. Ward

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Content

The notion of the classification scheme as a transitional element or "boundary object" (Star, 1989) offers an alternative to the more traditional approach that views classification as an organizational structure imposed upon a body of knowledge to facilitate access within a universal and frequently static framework. Recognition of the underlying relationship between user access and the collective knowledge structures that are the basis for knowledge production indicates the dynamic role of classification in supporting coherence and articulation across heterogeneous contexts. To this end, it is argued that the library should be an active participant in the production of knowledge, and that this role can be effected by the development of classificatory structures that can support the needs of a diverse information ecology consisting of a complex web of interacting agents, users, and technologies. Within such an information ecology, a classificatory structure cannot follow a one-size-fits-all paradigm but must evolve in cooperative interaction between librarians and their user groups.

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Imprint

Medford; NJ : Learned Information

Source

The economics of information. ASIS'94. Proc. 57th ASIS Annual Meeting, Alexandria, VA, Oct. 17-20, 1994. Ed.: B. Maxian

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Source

Annual review of information science and technology. 33(1998), S.131-186

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Source

Exploring the contexts of information behaviour: Proceedings of the 2nd International Conference on Research in Information Needs, Seeking and Use in Different Contexts, Sheffield, UK, 1998. Ed. by D.K. Wilson u. D.K. Allen

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Abstract

In information science, classification systems are conventionally viewed as tools for representing knowledge in the universe of ideas, the human mind, or one or more sets of documents. In this view, developing and maintaining relationships and structures in classification schemes must primarily consider two abstract ingredients: i) a set of concepts for one or more domains; and ii) a (set of) unambiguous structure(s) to articulate the relationships that persist between the various concepts that comprise the classificatory structure. We contend that design decisions pertaining to the structure of a classification system consist of far more than simply creating links between the elements in a particular set of concepts. Ultimately, a simplistic tool view of classifications implies that the construction is little more than a technical task in a very narrow sense: that classificatory concepts are viewed as standard representations of what are assumed to be the central and/or important topics in the knowledge domain(s), and that there is i) an unambiguous Platonic ideal or universal consensus that determines how the links will be generated within a classificatory structure; or, conversely, ii) that there are no general structures and relationships available at all, but that only diverse individual knowledge structures exist, which cannot be reconciled into a general organization of knowledge

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Abstract

Icons are graphic images with functional roles in human-computer interaction. They may be used as conceptual tools to represent the organization of information or as operators affecting an activity such as printing or moving to another document. An icon may represent its referent either as a sign, a purely arbitrary relationship that must be learned by the user; as a pictograph, a visual image of the object represented, or as an ideogram, whre the referent is not a concrete entity but an attribute, a set of attributes, or an abstract concept associated with the referent. The symbolicity of an icon reflects the drgree of representativeness that obtains between an icon and its referent(s). We propose to examine symbolic languages composed of sets of icons and to assess their effectiveness as classificatory structures in terms of: 1) representation of hierarchical structure; 2) level of symbolicity; 3) contexts that promote the capability of icons to represent organization; 4) relationship between an underlying metaphorical framework and iconic representation of the organization; 5) graphic elements of effective symbolic languages; and 6) social or cultural factors related to the effectiveness of icons

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Imprint

Medford : Learned Information Inc.