Download (direct link):
xmlns:personOnt="http://www.wam.umd.edu/~mhgrove/personOnt.rdf#"> <general1.0:Organization rdf:ID="Virtual_Knowledge_Base_">
<personOnt:Person rdf:ID="Ted_Wiatrak"></personOnt:Person> <personOnt:Person rdf:ID="Danny_Proko"></personOnt:Person> </rdf:RDF>
<b>Virtual Knowledge Base (VKB) </b>
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Listing 5.9 RDF embedded in HTML (via SMORE).
Listing 5.9 demonstrates the embedding of RDF in HTML using a script element. The script specifies that its contents are an RDF document using the RDF MIME type "application/rdf+xml". The RDF captures statements about the organizations, suborganizations, and people discussed in the HTML page.
A project from IBM's Knowledge Management Group and Stanford's Knowledge Systems Laboratory that enables the distributed processing of chunks of RDF knowledge is the TAPache subproject of the TAP project at http:// tap.stanford.edu. TAPache is a module for the Apache HTTP server that
enables you to publish RDF data via a standard Web service called getData(). This allows easy integration of distributed RDF data. This further highlights the ability to assemble context even from disparate servers across the network.
This section demonstrated several concepts and ideas that leverage RDF's strength in noncontextual modeling. The idea that context can be assembled in a bottom-up fashion is a powerful one. This is especially useful in applications where corporate offices span countries and continents. In the end, it is the end user that is demanding the power to assemble information as he or she sees fit. This building-block analogy in information processing is akin to the "do-it-yourself" trend of retail stores like Home Depot and Lowe's. The end user gets the power to construct larger structures from predefined definitions and a simple connection model among statements. In the end, it is that flexibility and power that will drive the adoption of RDF and provide a strong foundation layer for the Semantic Web.
In this chapter, we learned about the foundation layer of the Semantic Web called the Resource Description Framework (RDF). The sections built upon each other, demonstrating numerous applications of RDF, highlighting the strengths and weaknesses of the language, and offering ideas and concepts for leveraging it in your organization.
The first section answered the question "What is RDF?" It began by highlighting its most obvious use in describing opaque resources like images, audio, and video. We then began dissecting the technology into its core model, syntax, and additional features. The core model revolves around denoting concepts with Universal Resource Identifiers (URIs) and structured knowledge as a collection of statements. An RDF statement has three parts: a subject, a predicate, and an object. The RDF/XML syntax uses a striped syntax and a set of elements like rdf:Description and attributes like rdf:about, and rdf:resource. The other features discussed in the section were RDF containers and reification. RDF containers allow an object to contain multiple values or resources. RDF reification allows you to make statements about statements.
The second section cast a skeptic's eye on the slow adoption of RDF. We first noted this phenomenon by comparing RDF's adoption to XML's adoption via simple Web queries. We then listed several possible reasons for the slow adoption: the difficulties in combining RDF and XML documents, the complexity of RDF concepts and syntax, and the weakness of current examples like RSS and Dublin core that do not highlight the unique characteristics of RDF. However, we are confident that RDF's strengths outweigh its weaknesses and forecast
Understanding the Resource Description Framework
strong adoption in the coming year. Its two main engines of growth will be ontologies (like RDF Schema) and noncontextual modeling.
The third section covered the layer above RDF called RDF Schema. RDF Schema provides simple RDF subjects (classes) and predicates (properties) for defining new RDF vocabularies. This section demonstrated the power of RDF via the Protégé ontology editor and an example of how a good ontology models the key determinants of decision making that often get muddled or lost in free text descriptions. Thus, RDF strengthens the basic proposition of the Web: Adding meta data and structure to information improves the effectiveness of our processing and in turn our processes.
The final section of the chapter explored a powerful new trend called noncon-textual modeling. To define the concept, we began with its antonym, contextual modeling. We stressed the continuum between these two extremes and how neither is good or bad, just less or more appropriate to solving the particular business problem. Whereas document types provide context and implicit relationships supporting the document divisions and fields, noncontextual modeling builds its context by connecting its statements. In other words, either the context is fed onto the information or the context is derived from the information. We believe that noncontextual modeling and the merging of contextual and noncontextual modeling will rise exponentially in the next five years. This loosely coupled, slowly accrued knowledge that is supported by well-defined concepts and relationships specified in ontologies and knitted together from within and outside your organization will enable huge productivity gains through better data mining, knowledge management, and software agents.