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The two main drivers of practical knowledge management are technology and people, as pointed out earlier by Davenport (1996). Traditional IT-supported knowledge management applications are built around some kind of corporate or organizational memory (Abecker et al., 1998). Organizational memories integrate informal, semi-formal, and formal knowledge in order to facilitate its access, sharing, and reuse by members of the organization(s) for solving their individual or collective tasks (Dieng et al., 1999), for example as part of business processes (Staab and Schnurr, 2002). The knowledge structures underlying such knowledge management systems constitute a kind of ontology (Staab and Studer, 2004) that may be built according to established methodologies (Fernandez-Lopez et al., 1999; Sure, 2003). These methodologies have a centralized approach towards engineering knowledge structures requiring knowledge engineers, domain experts, and others to perform various tasks such as a requirement analysis and interviews. While the user group of such an ontology may be huge, the development itself is performed by a comparatively small group of domain experts who provide the model for the knowledge, and ontology engineers who structure and formalize it. Decentralized knowledge management systems are becoming increasingly important. The evolving Semantic Web (Berners-Lee et al., 2001)

This is the probability distribution function for the binomial random variable in general n x n x = (q+p)n = 1, We note that P(X = x) 0 and n P(X = x) = n x=0 x=0 x p q so the properties of a discrete probability distribution function are satis ed Graphs of binomial distributions are interesting We show some here where we have chosen p = 03 for various values of n (Figures 53, 54, and 55) The graphs indicate that as n increases, the probability distributions become more bell shaped and strongly resemble what we will call, in 8, a continuous normal curve This is in fact the case, although this fact will not be pursued here One reason for not pursuing this is that exact calculations involving the

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The ERSmsCmdReceiveNoMoreSms PDU needs no extra data it just signi es that all messages from the last speci ed get request have been returned The ERSmsCmdVersionReply PDU includes standard version and build data The ERSmsCmdMsvEvent PDU includes a series of sets of event data These include the event type and the relevant message index identi ers The ERSmsCmdReceiveInitialSms and ERSmsCmdReceiveMoreSms messages are the complex PDUs, as they include one or more SMS messages with all details Query Version Command.

Semantic Web Technologies: Trends and Research in Ontology-based Systems John Davies, Rudi Studer, Paul Warren # 2006 John Wiley & Sons, Ltd

035 03 025 02 015 01 005 0

Field Opcode Transaction ID Type Int32 Int32 Meaning ERSmsCmdQueryVersion (=1) PDU transaction identi er

will foster the development of numerous use cases for this new paradigm. Therefore, methodologies based on traditional, centralized knowledge management systems are no longer feasible. There are some technical solutions toward Peer-to-Peer knowledge management systems (e.g., Bonifacio et al., 2003; Ehrig et al., 2003). Still, the traditional methodologies for creating and maintaining knowledge structures appear to be unusable in distributed and decentralized settings, and so the systems that depend on them will fail to cope with the dynamic requirements of big or open user groups. The chapter is structured as follows. First, we de ne methodology and ontology engineering methodology in Section 9.2. Then we provide a survey of existing ontology engineering methodologies in Section 9.3. Since we believe that the engineering of ontologies in practical settings requires tool support to cope with the various complex tasks we also include a survey of corresponding ontology engineering tools in this section. The survey ends with an enumeration of open research issues. We partly address these research issues with the new DILIGENT (Distributed, Loosely controlled, and evolvInG Engineering of oNTologies) methodology which is introduced in Section 9.4. Before concluding we present some rst lessons learned from applying DILIGENT in a number of case studies.

p = 03

Field Opcode Transaction ID Major Version Minor Version Build Number Type Int32 Int32 Int32 Int32 Int32 Meaning ERSmsCmdVersionReply (=2) PDU transaction identi er Major version number Minor version number Build number

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