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2.3. ONTOLOGY DEFINITION
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Ontologies are used for organizing knowledge in a structured way in many areas from philosophy to Knowledge Management and the
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METHODOLOGY FOR SEMI-AUTOMATIC ONTOLOGY CONSTRUCTION
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Semantic Web. We usually refer to an ontology as a graph/network structure consisting from: 1. a set of concepts (vertices in a graph); 2. a set of relationships connecting concepts (directed edges in a graph); 3. a set of instances assigned to a particular concepts (data records assigned to concepts or relation). More formally, an ontology is de ned (Ehrig et al., 2005) as a structure O (C; T; R; A; I; V; C ; T ; sR ; sA ; iC ; iT ; iR ; iA ). It consists of disjoint sets of concepts (C), types (T), relations (R), attributes (A), instances (I), and values (V). The partial orders C (on C) and T (on T) de ne a concept hierarchy and a type hierarchy, respectively. The function sR : R ! C2 provides relation signatures (i.e., for each relation, the function speci es which concepts may be linked by this relation), while sA : A ! C T provides attribute signatures (for each attribute, the function speci es to which concept the attribute belongs and what is its datatype). Finally, there are partial instantiation functions iC : C2I (the assignment of instances to concepts), iT : T2V (the assignment of values to types), iR : R ! 2I I (which instances are related by a particular relation), and iA : A ! 2I V (what is the value of each attribute for each instance). Another formalization of ontologies, based on similar principles, has been described by Bloehdorn et al. (2005). Notice that this theoretical framework can be used to de ne evaluation of ontologies as a function that maps the ontology O to a real number (Brank et al., 2005).
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2.4. METHODOLOGY FOR SEMI-AUTOMATIC ONTOLOGY CONSTRUCTION
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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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KNOWLEDGE DISCOVERY FOR ONTOLOGY CONSTRUCTION
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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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