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538 Magnetism 588 Bryophyta
539 Modern physics 589 Thallobionta and Prokaryotae
540 Chemistry and allied sciences 590 Zoological sciences
541 Physical and theoretical chemistry 591 Zoology
542 Techniques, equipment, materials 592 Invertebrates
543 Analytical chemistry 593 Protozoa, Echinodermata, related phyla
544 Qualitative analysis 594 Mollusca and Molluscoidea
545 Quantitative analysis 595 Other invertebrates
546 Inorganic chemistry 596 Vertebrata (Craniata, Vertebrates)
547 Organic chemistry 597 Cold-blooded vertebrates: Fishes
548 Crystallography 598 Aves (Birds)
549 Mineralogy 599 Mammalia (Mammals)
If you were looking for a book on dinosaurs, you would probably look under Category 567 ("Fossil cold-blooded vertebrates") if you thought dinosaurs were reptiles ("cold-blooded"), or possibly under Category 568 ("Fossil Aves: Fossil Birds") if you thought dinosaurs were birds, or possibly under the more general Category 566 ("Fossil Vertebra: Fossil Craniata") if all you knew is that dinosaurs had backbones or if you knew that Fossil Craniata meant "animals having skulls." And if you knew that, then you probably knew that animals having skulls have backbones.
This discussion also demonstrates a difficulty: How do you map taxonomies to each other? Perhaps you want to map the Dewey Decimal categorization for dinosaur to the Linnaeus categorization (Class Diaspida or something below that?) and to the UNSPSC categorization (maybe "dinosaur bones" are a product that you can buy or sell; where would you classify it?). We look at the general problem of semantic mapping in the next chapter. Semantic mapping is a critical issue for information technologists considering using multiple knowledge sources. But let's return to what a taxonomy is.
A taxonomy, like a thesaurus or an ontology, is a way of structuring your data, your information entities, and of giving them at least a simple semantics. On the Web, taxonomies can be used to help your customers find your products and services. Taxonomies can also help you get a handle on your own information needs, by classifying your interests (whether they include products and services or not). Because taxonomies are focused on classifying content (semantics or meaning), they enable search engines and other applications that utilize taxonomies directly to find information entities much faster and with much greater accuracy. Back in 2000, Forrester Research published a report entitled "Must Search Stink?" (Hagen, 2000) In this study, Forrester Research answered its own question: If you really address search issues (read: content categorization) and use emerging best practices, search does not have to stink. In fact, taxonomies and other content representations will definitely improve search efficiency.
In Chapter 4, UDDI was introduced. In a real sense, UDDI requires taxonomies and ontologies. A directory or registry of Web products and services absolutely needs some way of classifying those products and services; otherwise, how can anything be found? UDDI has proposed the tModel (http://www/uddi.org) as the placeholder for taxonomies such as UNSPSC and the North American Industry Classification System (NAICS)1 that can be used to classify Web products and services. When you look in the Yellow Pages of a phone book, you see that under the Automobile heading are many other subheadings or categories: Automobile Accessories, Automobile Body Repairing and Painting, Automobile Dealers (New or Used), Automobile Parts and Supplies, Automobile Renting, Automobile Repair, and so on. This is a simple taxonomy. The entire Yellow Pages is a huge taxonomy. It is ordered alphabetically to be of additional assistance to a person looking for products or services, but its primary function is as a taxonomy classifying the available content. The Yahoo and Google taxonomies act in much the same way: They assist a user looking for content by categorizing that content as naturally (as semantically realistically) as possible.
1 The North American Industry Classification System (NAICS): http://www.census.gove/ epod/www/naics.html. See also its ongoing related effort at classifying products, the North American Product Classification System (NAPCS).
The next section will help you differentiate among the concepts related to taxonomies: schemas, thesauri, conceptual models, and ontologies. All of these expand on the simple classification semantics and structure expressed by taxonomies. In the next section, the Ontology Spectrum is introduced. This is a framework for comparing these concepts.
Defining the Ontology Spectrum
We discuss the notion of ontology and ontologies in greater detail in the next chapter, but this section introduces some crucial distinctions that we make in the general ontological/classificational space we call the Ontology Spectrum. We have discussed taxonomies up to this point. The subsequent sections of this chapter talk about Topic Maps and RDF, and their similarities and differences. But can Topic Maps and RDF enable you to represent taxonomies or ontologies? Or both or neither or something in between? Before we can make sense of the question and its possible answers, we need to make sure we understand what we are talking about—at least to a certain extent. Do we need to know everything about taxonomies and ontologies? No. But we need to know the basic distinctive properties of each and place each concept within some relative context of use. Taxonomies were defined in the previous section. Ontologies are defined in the next chapter. How do you distinguish them? We'll answer this question here.