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Current ontologies can vary greatly in a number of different ways, from content to structure and implementation: Content Type. Content-de ning terms can be typed and de ned at different levels of precision, from abstract (such as concepts) to speci c (or itemized) object detail, within the same conceptual domain. Description Level. This classi cation re ects the formalization span of models from simple descriptive lexicons and controlled vocabularies, to the more complex models where properties can de ne new concepts and relationships between terms. Conceptual Scope. Some models are restricted to more general, upper-level relationships, essentially free of domain-speci c relationships. Upper ontologies are used to bootstrap or graft more speci c ones that target very speci c domains in great detail. These differently-scoped models can work together, complementing each other for particular applications. Instantiation. Each model can draw its own distinction between the classical terminology concept and some point where it deals with instances of objects that manifest the terminology (that is, the model s assertional component). In practical implementation, the chosen instantiation line is where a knowledge database of speci c items can be extracted and maintained as a separate entity. Speci cation Language. Although ontologies can be built using ordinary programming languages, it is more common to use languages specially designed to support ontology construction or description logic. Some examples are given later in this chapter. As this list suggests, all ontologies are not created equal. Rather, each is constructed according to particular design goals and developer demands. It is fortunate therefore that the underlying intent of Web ontologies is for interoperability. If managed in the appropriate languages (XML-based), applications can translate between them, or use several at once to complement each other.

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A meta-analysis of eight methodologically rigorous DARE outcome evaluations, however, concluded that the programme s short-term effect on drug-using behaviour was small (Ennett et al, 1994) The authors noted the disparity between the programme s popularity and prevalence and its effectiveness, and indicated that this could mean that it was taking the place of other more bene cial drug education interventions Lindstrom & Svensson (1998) reported similar ndings in their evaluation of the Swedish equivalent of DARE, the VAGA programme They found that students who had undertaken the programme were no different in their attitudes to drugs or actual drug-using behaviour than students who had not participated On the basis of their ndings, they questioned the emphasis of the programme and the use of limited police resources for this task.

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4. assign outputD = (inputD <= 2); outputD is at logic 1 when inputD has a value of either 0 , 1 , or 2 . This means that outputD is a logic 1 when the values at inputD is less then or equal to 2 . 5. assign outputE = (inputA > inputB); outputE is at logic 1 (clock 9 to clock 16) when: inputA has a value of 2 and inputB has a value of 1 (clock 9 to clock 12) inputA has a value of 3 and inputB has a value of 0 (clock 13 to clock 16) This means that outputE is a logic 1 when inputA is greater than inputB. 6. assign outputF = (inputB < inputC); outputF is at logic 1 when: inputB has a value of 2 and inputC has a value of 3 (clock 8) inputB has a value of 1 and inputC has a value of 2 or 3 (clock 11, clock 12) inputB has a value of 0 and inputC has a value of 1 , 2 , or 3 (clock 14, clock 15, clock 16) This means that outputF is a logic 1 when inputB is less than inputC

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