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Abstract

To Classify is (according to Webster's) "to distribute into classes; to arrange according to a system; to arrange in sets according to some method founded on common properties or characters." A model of classification is a type or category or (excuse the recursive definition) a class of classification "system" as mentioned in Webster's definition. One employs a classification model to implement a specific classification system. (E.g. we employ the hierarchical classification model to implement the Dewey Decimal System) An effective classification model must represent both the commonality (Webster's "common properties"), and also the differences among the items being classified. The commonality of each category or class defines a test to determine which items belong to the set that class represents. The relationships among the classes define the variability among the sets that the classification model can represent. Therefore, a classification model is more than an enumeration or other simple listing of the names of its classes. Our purpose in employing classification models is to build metadata systems that represent and manage knowledge, so that users of these systems we build can: quickly and accurately define (the commonality of) what knowledge they require, allowing the user great flexibility in how that desire is described; be presented existing information assets that best match the stated requirements; distinguish (the variability) among the candidates to determine their best choice(s), without actually having to examine the individual items themselves; retrieve the knowledge they need The MetaData model we present is Contextual Classification. It is a synthesis of several traditional metadata models, including controlled keyword indices, hierarchical classification, attribute value systems, Faceted Classification, and Evolutionary Faceted Classification. Research into building on line library systems of software and software documentation (Frakes and Pole, 19921 and Pole 19962) has shown the need and viability of combining the strengths, and minimizing the weaknesses of multiple metadata models in the development of information systems. The MetaData Object Manager (M.O.M.), a MetaData Warehouse (MDW) and editorial work flow system developed for the Thomson Financial Publishing Group, builds on this earlier research. From controlled keyword systems we borrow the idea of representing commonalties by defining formally defined subject areas or categories of information, which sets are represented by these categories names. From hierarchical classification, we borrow the concept of relating these categories and classes to each other to represent the variability in a collection of information sources. From attribute value we borrow the concept that each information source can be described in different ways, each in respect to the attribute of the information being described. From Faceted Classification we borrow the concept of relating the classes themselves into sets of classes, which a faceted classification system would describe as facets of terms. In this paper we will define the Contextual Classification model, comparing it to the traditional metadata models from which it has evolved. Using the MOM as an example, we will then discuss both the use of Contextual Classification is developing this system, and the organizational, performance and reliability

Imprint

Medford, NJ : Information Today

Series

Proceedings of the American Society for Information Science; vol.36

Source

Knowledge: creation, organization and use. Proceedings of the 62nd Annual Meeting of the American Society for Information Science, 31.10.-4.11.1999. Ed.: L. Woods