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A production line has been producing good parts with probability 085 A sample of 20 parts is taken, and it is found that 4 of these are defective Assuming a binomial model, is this a cause for concern

Let X denote the number of good parts in a sample of 20 Assuming that the probability a good part is 085, we nd the probability the sample has at most 16 good parts is

In the following we summarize the distinctive features of the available ontology engineering methodologies and give quick pointers to existing tool support speci cally targeted to the methodologies. Next, we brie y introduce the most prominent existing tools.

Message ID Int32 If the message index identi er is zero then this is the last message in the buffer and there is no more message data Index identi er of the parent folder Date and time stamp of the message

P(X 16) =

An extensive state-of-the-art overview of methodologies for ontology engineering can be found in Gomez-Perez et al. (2003). More recently Cristani and Cuel (2005) proposed a framework to compare ontology engineering methodologies and evaluated the established ones accordingly. In the OntoWeb2 project, the members gathered guidelines and best practices for industry (Leger et al., 2002a, b) with a focus on

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Message description up to the rst 32 characters of the body text Length in characters of message detail Message detail rst recipient address

So this event is not unusual and one would probably conclude that the production line was behaving normally and that although the percentage of defective parts has increased, the sample is not a cause for concern

see http:/ /www.ontoweb.org/

A sample survey of 100 voters is taken where actually 45% of the voters favor a certain candidate What is the probability that the sample will contain between 40% and 60% of voters who favor the candidate We presume that a binomial model is appropriate Note that the sample proportion of voters, say ps , can be expressed in terms of the number of voters, say X, who favor the candidate In fact, ps = X/100, so P(040 ps 060) = P 040 X 060 100

Name Length Recipient Name Address Length Recipient Address Int16 Unicode data Int16 Unicode data Length in characters of recipient name Recipient name

= P(40 X 60)

applications for E-Commerce, Information Retrieval, Portals and Web Communities A very practical oriented description to start building ontologies can be found in Noy and McGuinness (2001) In our context, the following approaches are especially noteworthy Where it is adequate we give pointers to tools mentioned in the next section, whenever tool support is available for a methodology CommonKADS (Schreiber et al, 1999) is not per se a methodology for ontology development It covers aspects from corporate knowledge management, through knowledge analysis and engineering, to the design and implementation of knowledge-intensive information systems CommonKADS has a focus on the initial phases for developing knowledge management applications CommonKADS is therefore used in the OTK methodology for the early feasibility stage For example, a number of worksheets can be used to guide a way through the process of nding potential users and scenarios for successful implementation of knowledge management.

Length in characters of recipient address Recipient address (i.e. mobile phone number)

x=40

CommonKADS is supported by PC PACK, a knowledge elicitation tool set, that provides support for the use of elicitation techniques such as interviewing, that is it supports the collaboration of knowledge engineers and domain experts DOGMA (Jarrar and Meersman, 2002; Spyns et al, 2002) is a databaseinspired approach and relies on the explicit decomposition of ontological resources into ontology bases in the form of simple binary facts called lexons and into so-called ontological commitments in the form of description rules and constraints The modeling approach is implemented in the DOGMA Server and accompanying tools such as the DOGMA Modeler tool set The Enterprise Ontology (Uschod and King, 1995; Uschold et al.

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