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Semantics and the Evolution of Specialized Languages : Page 3

Precise semantics for data elements allow sets of highly specialized languages to evolve quickly, giving you concise, little languages as an alternative to procedural languages for creating business applications.

Getting the Picture
Resource Description Framework, the language of graphs. The foregoing discussion has involved only those languages that manipulate XML tree structures. The semantic web requires more than just trees in stand-alone documents. The semantic web depends on graphs and inference to allow two independent web pages to be joined together to create new information. Inference is a fundamental tenant to the semantic web that allows graphs extracted from different web pages to be merged, just as SQL merges data from tables. Graphs are best stored in nodes and the connections among them. In Resource Description Framework (RDF) nodes are resources, and the links among the nodes are properties.

This (node-link-node) arrangement is one of the main structures for representing graphs on the semantic web, and RDF is the language for doing this representation. RDF has unfortunately developed a reputation for being difficult to learn and difficult for humans to read. Much of this reputation has to do with the fact that for RDF joins to work a sometimes very lengthy URI (or URL) must represent each node and arc. RDF and its extensions—RDF Schema (RDFS)—also have a very small vocabulary of terms and are easy to create using XSLT. And RDF has its own query language: SPARQL.

RDF is beyond the scope of this discussion, but keep in mind that it is an important tool for representing knowledge on the semantic web.

Simple knowledge organization system. Although most of the semantic web community has agreed that RDF is critical to web-based search and inference, the jury is still out on exactly how higher-order logic, rules, and other systems will evolve. Standards such as Web Ontology Language (OWL) have gained a great deal of acceptance in some communities, but there are still opportunities for small declarative vocabularies to be very useful on top of the XML and RDF infrastructure.

However, there is a newer standard that has been gaining popularity for storing structured business vocabularies: simple knowledge organization systems (SKOS). Unlike other W3C standards that have reached the recommendation phase, SKOS is still in the working-draft stage.

SKOS represents structured, controlled vocabularies: thesauri, classification schemes, taxonomies, or subject-heading systems. SKOS has wide support for many corporate metadata librarians trying to classify and maintain data elements within large organizations. It is different than many XML standards because it assumes an RDF structure and allows RDF inference and query technologies to be used. Table 1 provides a list of SKOS RDF classes and properties.

Table 1. SKOS RDF Classes and Properties

Class or Property Name Type Purpose
Concept Class Declare that a resource is a concept or conceptual resource
Property, labeling: alternative and preferred lexical Assign preferred or alternative lexical labels to resources
altSymbol prefSymbol Property, labeling: alternative and preferred symbolic Assign preferred or alternative symbolic labels (images) to concepts
HiddenLabel Property, labeling: hidden lexical Assign hidden lexical labels to resources to make character strings accessible for text-based indexing and searching applications
Property, documentation Add human-readable documentation to a concept's description
Property, semantic relation Declare semantic—or paradigmatic—broad, narrow, or associative relationships between concepts
ConceptScheme Class, concept scheme Declare that a resource is a concept scheme
Property, concept scheme Declare that a concept is part of a concept scheme or declare a link between them
Property, concept scheme: subject indexing Index information resources on the web by subject
Collection Class, meaningful collection of concepts Assign lexical labels to specific collections
member Property, meaningful collection of concepts With the Collection class, assign lexical labels to specific collections
OrderedCollection Class, meaningful collection of concepts (subclass of Collection) Define ordered collections of concepts
memberList Property, meaningful collection of concepts With the OrderedCollection class, define ordered collections of concepts
subjectIndicator Property, published subject indicators Declare links between concepts and human-readable documents that describe the content

Source: W3C: SKOS Core Guide, W3C Working Draft 2, November 2005

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