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This chapter also introduced Topic Maps and the various TM standards. Any given topic map is at least a taxonomy in the sense that it tries to say something about how subjects are structured and related, using the notions of topics and associations. One can have multiple topic maps covering the same collection of Web and non-Web objects, just as one can have multiple indexes of the same document or documents.
If Topic Maps is a way of describing and structuring an information space in terms of topics and associations, then, in contrast, RDF is a Web language for describing and structuring an information space in terms of resources and properties. But after revisiting what RDF is—and to a limited extent, introducing some aspects of RDF Schema, which we look at more closely in the next chapter—we saw that Topic Maps and RDF actually have many similarities. The primary differences between the two paradigms are (1) they were developed by different communities for slightly different classification tasks and (2) RDF has a schema level (RDF Schema) that enables you to describe a set of properties and the relationships between these properties and other resources—in other words, a meta model to the RDF object model—whereas Topic Maps currently does not have such a level. With the eventual development of the Reference Model and a Topic Map Constraint Language, however, this latter distinction may be weakened.
As we shall see in the next chapter, RDF and Topic Maps pave the way for increasing the representational capabilities of an information model over that of a taxonomy. Both paradigms provide some of the essential building blocks for constructing the semantically richer notion of ontologies.
"Ontology is the very first science. Ontology involves discovering categories and fitting objects into them in ways
that make sense When we make a list of things to do,
or of records and books we most want to buy, or videos we intend to rent, we are categorizing—we are engaging in rudimentary ontology. By prioritizing items in a list, we are assigning relationships among various things. Ontology can be relatively simple, or it can be quite complex.
Ontology becomes more complex, and even daunting, when we begin to grapple with large domains of objects with complex relationships among them. For instance, anyone who has attempted to outline the processes and components of even a relatively small enterprise has experienced the brain-cramps that can come with complex ontology."
—David Koepsell, Center for Commercial Ontology: Prospectus http://www.acsu.buffalo.edu/~koepsell/center.htm
Ontologies are about vocabularies and their meanings, with explicit, expressive, and well-defined semantics—possibly machine-interpretable. So what does this statement mean? What's a vocabulary? What's a meaning? What is semantics? What does machine-interpretable mean? What is ontology and what are ontologies? In this chapter, we define what ontology is and what ontologies are in clear and simple language, with meaningful examples. You may discover many ideas that are strange at first, such as semantics, knowledge representation, domain, reference, truth-function, intension, extension, axiom, theorem, theory, but you will be given useful, incisive, and simple explanations of what those ideas are, how they can be used in practice in your information technology projects, and where semantic technologies are heading.
You will also be happy to know that ontologies do have something to do with taxonomies, discussed in the previous chapter. In fact, ontologies extend taxonomies quite some way. Ontologies are to taxonomies as two-dimensional space is to three- (or more) dimensional space. In other words, ontologies enable you to specify the semantics of your domain, your enterprise, or your community, or across many communities, in great and arbitrarily greater detail. You'll also learn a bit about some languages used to express ontologies, including the W3C's emerging Web Ontology Language (OWL).
Overview of Ontologies
So what is ontology, and what are ontologies? Before looking at some definitions, let's take a look at an actual ontology.
Figure 8.1 shows a simple human resources ontology created in the ontology management tool called Protégé (http://protégé.stanford.edu). You'll notice that there are classes such as Person, Organization, and Employee. In an ontology, these are really called concepts, because it is intended that they correspond to the mental concepts that human beings have when they understand a particular body of knowledge or subject matter area or domain (these phrases are all used interchangeably; they are intended to be synonymous), such as the human resources domain.
These concepts and the relationships between them are usually implemented as classes, relations, properties, attributes, and values (of the properties/ attributes). So what Figure 8.1 depicts primarily are concepts of the important entities of the domain, which are implemented as classes. Examples are Person, Organization, and Employee. Also depicted are the relations between these entity-focused concepts, such as employee_of, managed_by, and manages. Finally, properties or attributes are depicted. Examples include address, name, birthdate, and ssn under the Person class. These properties or attributes have either explicit values or, more often, have value ranges. The value range for the property/attribute of employee_of, a property of the class Employee, for example, is the class Organization. By range we mean that the only possible values for any instances of the property employee_of defined for the class Employee must come from the class Organization.