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Figure 7.11 RDF statement as a graph.
Comparing Topic Maps and RDF
Both Topic Maps and RDF attempt to describe the information content of Web objects in terms of resources. Both standards exist in order to establish content meta data (data being about other data) about Web objects, to make those objects and their content more easily accessible. In Topic Maps, a topic is a Web object having occurrences (defined as resources—i.e., arbitrary information about the topic). The subject of the topic itself is represented by an occurrence of a resource, which can be addressable or not. Recall that an addressable subject is a Web object; a nonaddressable, indicated subject is not a Web object. Topics are linked by associations, and each topic in an association has a particular role that it plays in that association. But RDF was explicitly developed to enable the description (and linkage) of meta data to Web objects, whereas Topic Maps was meant to enable multiple content-based indexing of documents. If that distinction is kept in mind, then Topic Maps and RDF can be seen to be complementary paradigms. If indexing (or overlays of topic structure) represent the linking of subjects, then in fact it might be the case that RDF could represent the set of assertions that attempt to constitute the meaning of those subjects. In that case, Topic Maps and RDF can equitably coexist, each borrowing on the other's strengths and purposes.
In RDF, a resource (subject) has a property (predicate, relation), which has a property-value (object), which in turn can be a resource. This complicates the picture somewhat, at least with respect to Topic Maps, insofar as Topic Maps doesn't have this same notion of a resource's property itself being a resource, which by definition can have its own properties. And so on. This kind of linking means that RDF is a bit more complicated than Topic Maps. Whether Topic Maps evolves to have comparable machinery remains an open question. Currently, it is probably easier to represent a given complicated topic map in RDF than it is to represent a complicated RDF set of assertions in Topic Maps.
Table 7.5 shows the closest comparable constructs between Topic Maps and RDF.
The table cannot do real justice to the mappings between the constructs in these two paradigms, since, in general, so many qualifications would have to be made about the comparable equivalence between a topic and a resource (Is a topic really a resource? Is a resource about a subject as a topic is? Isn't the mapping of these constructs more along the lines of a mapping between comparable triples?) that the ultimate comparison is more suggestive than real.
Topic Maps does not yet have a defined Reference Model (RM), whereas RDF currently has RDF Schema, which is another distinction between the two paradigms. RDF Schema is a meta level or more abstract model that describes the object level of RDF. When the RM is defined (possibly with assistance from the Topic Map Constraint Language, itself under development), it may then be that the two paradigms have more comparable, formal power in defining assertions about topics and associations or resources and properties in terms of the semantics of those assertions. This is a topic (pun slightly intended) that we address in more detail in the next chapter.
Table 7.5 Comparing Topic Maps to RDF
TOPIC MAPS CONSTRUCTS RDF CONSTRUCTS
20 Occurrence in the Topic Maps paradigm is, strictly speaking, more like an instance in the object-oriented or ontology paradigms. With respect to RDF, a TM occurrence, because it is something that is relevant to a topic, can really be either a resource or property, simply because an instance in RDF is a triple specifying a specific object having a specific property/relation to another specific object—that is, a resource having a property and a value for that property (all of which can technically be resources).
21 An association is a relation between subjects (i.e., topics). As such, perhaps a better understanding is that is it is a type of property under the RDF perspective.
22 Although a subject is technically not a first-class construct in Topic Maps, because it crucially stands behind the notion of topic, which is the first-class notion, we include it in the comparison.
A taxonomy is a hierarchic classification (typically in a tree structure) of real-world objects. In information technology, a taxonomy is used to classify the information correlates of those objects. Because taxonomies are so closely related to other classification, vocabulary description, and information model representations, this chapter also described a framework called the Ontology Spectrum. The Ontology Spectrum distinguishes taxonomies from other representations in this space: thesauri, conceptual models, and logical theories. Taxonomies are important because they help structure and provide at least a simple semantics for an information space.