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Build Knowledge Through a Taxonomy of Classifications : Page 2

Learn the natural progression of classification systems and their associated taxonomies to help users find information and manage resources.

Shifting Taxonomies
In the design and development of web sites, the shift to a hierarchy usually starts innocently enough. One of a linear list of pages starts to become a table of contents for other pages in that particular category, and then another linear page gains children, and so on. At some point, this implicit folding gets incorporated into the navigation system. Yet, as the number of items increase, the level of folding within the categorization structure itself increases as well, and what had been a fairly simple, two-tier system begins to become bushy. Note that in pure folder/file structures the categorization typically mirrors the physical arrangement of files in the file system; though, as more of the web is being generated through server processes, that system is beginning to be overtaken by more sophisticated taxonomic schemes.

The third form of classification is network taxonomy (see Figure 3). In this form of classification terms in the taxonomy are defined by a set of keywords. Two objects that have the same term are connected on that term. In the simplest case where each object can have only one term, this classification degenerates into a linear taxonomy. On the other hand, if an object can have multiple terms associated with it, the object becomes a node in a network of taxonomic terms, creating ad hoc "definitions" where clusters of objects have related terms. For instance, a cat may have the taxonomic terms (or keywords) "furry," "carnivore," "quadriped," "tailed," and so forth; a dog may have the same keywords and, within the extent of the taxonomy, may be considered as part of the same group. However, the clustering breaks down if the terms "says woof" and "says meow" are added to the taxonomy. (It's worth noting here that a term is not necessarily a single word, as this example illustrates).

Figure 3. Network (Cloud)Taxonomy: In a network (cloud) taxonomy the links among terms are defined less by containment or categorization and more by metaphoric similarity, though this similarity may be defined by frequency of association rather than synonym-based similarity. The boundaries between cloud taxonomies and search consequently can become very amorphous and fuzzy.

Of course, note also that such keyword taxonomies can be turned into hierarchies if a hierarchical name is defined as being the name of all objects that contain a set of keywords from the total networked space of keywords. Thus, an animal might be considered a member of "cat" if it has the keywords "mammal," "meat-eater," "quadripedal," "purrs," and "fondness for dropping birds on doorsteps." From a site organization, your "cat" section would then contain content where all articles have these keywords in common, while your "dog" section may be in a related part of the site (the one that has "mammal," "meat-eater," and "quadripedal" as a higher-level organization), but one that is differentiated by having "barks" and "chases sticks." Those articles that have neither (an article about a parrot, for instance) would then fall into the catch-all "other" categorization.

Within networked taxonomies there are two important subdivisions (notice that even taxonomies can be categorized): controlled vocabularies and free vocabularies. Controlled vocabularies assume a fixed set of terms to choose from, usually set up by the taxonomist. This set ensures that the number of clusters of taxonomic terms remains comparatively low, making it possible to create hierarchical labels for such property sets. Many business vocabularies—such as the Universal Business Language (UBL), the XML Business Reporting Language (HBRL), Health Level 7 (HL7), and others—work on this principle of organization, not only enabling clustering but also allowing the cluster names themselves to remain within the overall taxonomy.

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