Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL
Dean Allemang, James Hendler
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The promise of the Semantic Web to provide a universal medium to exchange data information and knowledge has been well publicized. There are many sources too for basic information on the extensions to the WWW that permit content to be expressed in natural language yet used by software agents to easily find, share and integrate information. Until now individuals engaged in creating ontologies-- formal descriptions of the concepts, terms, and relationships within a given knowledge domain-- have had no sources beyond the technical standards documents.
Semantic Web for the Working Ontologist transforms this information into the practical knowledge that programmers and subject domain experts need. Authors Allemang and Hendler begin with solutions to the basic problems, but don’t stop there: they demonstrate how to develop your own solutions to problems of increasing complexity and ensure that your skills will keep pace with the continued evolution of the Semantic Web.
• Provides practical information for all programmers and subject matter experts engaged in modeling data to fit the requirements of the Semantic Web.
• De-emphasizes algorithms and proofs, focusing instead on real-world problems, creative solutions, and highly illustrative examples.
• Presents detailed, ready-to-apply “recipes” for use in many specific situations.
• Shows how to create new recipes from RDF, RDFS, and OWL constructs.
astrological use, and the twentieth-century astronomical use. This seems like a simple requirement, but until it is met, we can’t even talk about the relationship among these terms. We will see details of the Semantic Web solution to this issue in Chapter 3, but for now, we will simply prefix each term with a short abbreviation of its source—for example, use IAU:Planet for the IAU use of the word, horo:Planet for the astrological use, and astro:Planet for the twentieth-century astronomical use.
Web that allowed it to grow at such an unprecedented rate. To implement the Semantic Web, we need a model of data that allows information to be distributed over the Web. DISTRIBUTING DATA ACROSS THE WEB Data are most often represented in tabular form, in which each row represents some item we are describing, and each column represents some property of those items. The cells in the table are the particular values for those properties. Table 3.1 shows a sample of some data about works completed
face this issue. An important variant of “just in time” inferencing is where no explicit inferencing is done at all. We already saw, in our example about subclasses of Shirts, how a query could explicitly express what data it wanted, without relying on the inference semantics of the model at all. As we see in the next section, even in this case, where there is no explicit inferencing, the inference interpretation of a model is still important in organizing and understanding a semantic
:SeparateEggs; :hasPrerequisite :SliceBean; :hasPrerequisite :HeatCream; :hasPrerequisite :BeatEggs. For prerequisiteFor, we get the following inferences: :GraduallyMix :prerequisiteFor :AddMilk; :prerequisiteFor :CookCustard; :prerequisiteFor :TurnInFreezer; :prerequisiteFor :Chill. Now, for otherStep, we get the combination of these two. Notice that neither list includes Gradually Mix itself, so it does not appear in this list either. :GraduallyMix :otherStep :AddMilk; :otherStep
“Factory”. “Factory”. “Machine Shop”. “Machine Shop”. “Machine Shop”. Although we have global identifiers for individuals in these tables, those identifiers are not the same. For instance, p:Product1 is the same as mfg:Product4 (both correspond to model number B-1430). How can we cross-reference from one table to the other? The answer is to use a series of owl:sameAs triples, as follows: p:Product1 p:Product2 p:Product4 p:Product5 p:Product7 p:Product8 owl:sameAs owl:sameAs owl:sameAs