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Knowledge Discovery technologies can be used to support different phases and scenarios of semi-automatic ontology construction. We believe that today a completely automatic construction of good quality ontologies is in general not possible for theoretical, as well as practical reasons (e.g., the soft nature of the knowledge being conceptualized). As in Knowledge Discovery in general, human interventions are necessary but costly in terms of resources. Therefore the technology should help in ef cient utilization of human interventions, providing suggestions, highlighting potentially interesting information, and enabling re nements of the constructed ontology. There are several de nitions of the ontology engineering and construction methodology, mainly based on a knowledge management perspective. For instance, the DILIGENT ontology engineering methodology described in 9 de nes ve main steps of ontology engineering: building, local adaptation, analysis, revision, and local update. Here, we de ne a methodology for semi-automatic ontology



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We note that the waiting time can only range from zero to (TR 2 ), so we can rewrite equation (19.22) as: WC ( ) =

t 2 f C (t) dt WC ( )





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construction analogous to the CRISP-DM methodology (Chapman et al., 2000) de ned for the Knowledge Discovery process. CRISP-DM involves six interrelated phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. From the perspective of Knowledge Discovery, semi-automatic ontology construction can be de ned as consisting of the following interrelated phases: 1. domain understanding (what is the area we are dealing with ); 2. data understanding (what is the available data and its relation to semiautomatic ontology construction ); 3. task de nition (based on the available data and its properties, de ne task(s) to be addressed); 4. ontology learning (semi-automated process addressing the task(s) de ned in the phase 3); 5. ontology evaluation (estimate quality of the solutions to the addressed task(s)); and 6. re nement with human in the loop (perform any transformation needed to improve the ontology and return to any of the previous steps, as desired). The rst three phases require intensive involvement of the user and are prerequisites for the next three phases. While phases 4 and 5 can be automated to some extent, the last phase heavily relays on the user. Section 2.5 describes the fourth phase and some scenarios related to addressing the ontology learning problem by Knowledge Discovery methods. Using Knowledge Discovery in the fth phase for semi-automatic ontology evaluation is not in the scope of this , an overview can be found in (Brank et al., 2005).

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(19.23)

He then looked off into the distance and proceeded to tell me a longwinded story about how he had been out of college for six months, then was hired into a major bank, and how he did the working tour, like all the recent graduates, of the different parts of the bank At the end of his sixmonth tour, he started working at the equities trading desk Then he lost a bunch of money, almost got himself fired, and ended up with a story far more boring and far less informational than everything I had already learned When he was finished, he patted the desk in front of him Harry, if you d like to come down here after work and talk with some of the traders, that would be fine with me.

Motivated by simulation results, we assume that f C (t) is truncated exponentially distributed: f C (t) = (1 e

generation of ontology extraction systems, including for example Text2Onto (Cimiano and Volker, 2005), will explicitly target the problem of incremental ontology learning.

(T R 2 ) WC ( )

)WC ( )

When you re ready to start trading again, we ve can put a computer down here for you and you can access your account from here What was he talking about I wondered Why had he offered this Had he lost his mind It had to be that he knew Harvey Winklestein, and I was getting the royal treatment because of it But I thought I had remembered that Harvey hadn t ever met, or didn t know, or didn t like, Anderson Maybe Craig had talked with him Certainly I wasn t going to spend all day thinking about it, though I readily accepted..

In this section, we will describe how information on the usage of ontologies can be analyzed to recommend changes to the ontology The usage analysis that leads to the recommendation of changes is a very complex activity First, it is dif cult to nd meaningful usage patterns For example, is it useful for an application to discover that many more users are interested in the topic industrial project than in the topic research Second, when a meaningful usage pattern is found, the open issue is how to translate it into a change that leads to the improvement of an application For example, how to interpret the information that a lot of users are interested in industrial research project and basic research project, but none of them are interested in the third type of project applied research project.

e WC ( )

(19.24)

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