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User interest pro les, which are also stored in the ontology, are constructed from an analysis of user interaction with the BT digital library (from the digital library Web server log les) and from the content of the Web pages that a user accesses Software within the user s Web browser analyses documents accessed (for example, treating them as bags of words ) and creates a vector representing the user s interests These vectors are mapped to the most relevant topics in the BT digital library ontology In turn, the topics are then added to the user s pro le under the control of the user The ontology store includes not just the PROTON ontology but also a set of rules to be run when a query is executed These rules can be used to enable sophisticated query facilities, and also to enable a mapping between the ontologies.

The facts about probabilities, as shown in the previous section, are fairly easy The dif culty arises when we try to apply them The rst step in any probability problem is to de ne an appropriate sample space More than one sample space is possible; it is usually the case that if order is considered, then the desired probabilities can be found, because that is the most detailed sample space one can write, but it is not always necessary to consider order Let us consider the examples for which we previously found sample spaces

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The sockets library supports opening a random port number for listening, but the server then needs some way to communicate the port number to the PC client In Symbian OS v80 the problems of starting a server and communicating a port number are solved by using a Service Broker This is a server that listens on a xed socket (with the port number set by the IANA) and supports commands that will start a server by name and then return the port number on which it is listening In this way, the Service Broker needs to be active whenever the PC is connected and needs to have a xed port number, but the same is not true of any other Connectivity Socket Server.

Components in the semantic layer augment the ABI, Inspec and Web data with supplementary metadata The named entity identi cation and annotation components identify named entities such as people s names,.

In Example 11, we examined an item emerging from a production line and observed the result It might be sensible to assign the probabilities to the events as P(G) = 1/2 and P(D) = 1/2 if we suppose that the production line is not a very good one This is an example of a binomial event (where one of the two possible outcomes occurs at each trial) but it is not necessary to assign equal probabilities to the two outcomes It is a common error to presume, because a sample space has n points, that each point has probability 1/n For another example, when a student takes a course she will either pass it or fail it, but it would not be usual to assign equal probabilities to the two events But if we toss a fair coin, then we might have P(Head) = 1/2 and P(Tail) = 1/2 We might also consider a loaded coin where P(H) = p and P(T ) = q = 1 p where, of course, 0 p 1 It is far more sensible to suppose in our production line example that P(G) = 099 and P(D) = 001 and even these assumptions assume a fairly poor production line In that case, and assuming the events are independent, we then nd that P(GGDDG) = P(G) P(G) P(D) P(D) P(G) = (099)3 (001)2 = 0000097

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