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We see that if the rst success occurs at trial number x, then it must be preceded by exactly x 1 failures, so we conclude that P(X = x) = qx 1 p for x = 1, 2, 3, is the probability distribution function for the random variable X This is called a geometric probability distribution function The probabilities are obviously positive and their sum is S = p + qp + q2 p + q3 p + = 1 which we showed in 2 by multiplying the series by q and subtracting one series from another The occurrence of a geometric series here explains the use of the word geometric in describing the probability distribution We also state here that E[X] = 1/p, a fact that will be proven in 7 For example, if we toss a fair coin with p = 1/2, then the expected waiting time for the rst head to occur is 1/(1/2) = 2 tosses The expected waiting time for our new driver to pass the driving test is 1/(8/10) = 125 attempts We will consider the generalization of this problem in 7

In fact, we conjecture that the majority of knowledge sharing cases falls into this category. In particular in the Semantic Web context, we nd these requirements.

This has been a brief introduction to four of the most commonly occurring discrete random variables There are many others that occur in practical problems, but those discussed here are the most important

a reference to an SMS client MTM a Rich Text object that we will use for body text a Waiter object that we will need in order to manage asynchronous Message Server operations. We create these in a class CRSmsMsg that we will be able to use in both types of PC Connectivity server. This class will use standard two-phase construction.

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community of users is much larger than the community of active ontology builders Birds-eye view: An initial ontology is built by a small group This ontology is made available and users are free to use and modify it locally for their own purposes There is a central board that maintains and assures the quality of the shared core ontology This central board is also responsible for updating the core ontology (see open issue 1) However, updates are mostly based on changes reoccurring at and requests by locally working users Therefore, the board only loosely controls the process Due to the changes introduced by the users over time that entail the changes introduced by the board, the ontology evolves Let us now survey the DILIGENT process at a ner level of granularity.

5

class CRSmsMsg : public CBase { public: static CRSmsMsg *NewL(CMsvSession *aMsvSession); CRSmsMsg(); void ConstructL(CMsvSession *aMsvSession); virtual CRSmsMsg(); private: CMsvSession* iMsvSession; // Message Server Session CClientMtmRegistry* iMtmRegistry; CSmsClientMtm* iSmsMtm; CParaFormatLayer* iParaFormatLayer; CCharFormatLayer* iCharFormatLayer; CRichText* iRichText; CMsvOperationWait* iWaiter; };

DILIGENT comprises ve main steps: (1) build, (2) local adaptation, (3) analysis, (4) revision, (5) local update (see Figure 91) Build: The process starts by having domain experts, users, knowledge engineers, and ontology engineers build an initial ontology In contrast to existing ontology engineering methodologies (cf Section 931), we do not require completeness of the initial shared ontology with respect to the domain The team involved in building the initial ontology should be relatively small, in order to easily nd a small and consensual rst version of the shared ontology Local adaptation: Once the core ontology is available, users work with it and, in particular, adapt it locally to their own needs Typically, they will have their own business requirements and correspondingly change and adapt their local copy of the shared ontology (Noy and Klein, 2003; Stojanovic et al, 2002).

We will soon return to random variables, but only to random variables whose probability distributions are continuous For now, we pause and consider an application of our work so far by considering seven-game series in sports Then we will return to discrete probability distribution functions

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