P. Zellweger (USA)
Intelligent systems, database, knowledge representation, knowledge modeling, RDBMS, taxonomy, database mapping, data structures.
This paper introduces the notion of a database taxonomy. Unlike other types of taxonomies, this taxonomy is derived almost entirely from data in a database according to the relations cast by its data modeling. Taking a cue from data mining, the author employs a data structure to capture data in an RDBMS according these relationships in order to produce nested topic lists for the taxonomy. The combination of this data structure and its nested mapping algorithms transform selected lists of data, or information, into this new type of knowledge representation. End-users view this database taxonomy from an interface that functions like database navigation structure. These advances redefine database retrieval capabilities, as we know them, by enabling users to browse and explore database content by visualizing the data and its relations along paths supported by the conceptual model. More importantly, these advances enable end-users navigate these access paths to pinpoint and retrieve the exact information they need. In addition, this type of knowledge representation or modeling capability lays the technical framework for solving other, more challenging data access issues, namely database interoperability.
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