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Figure 4.1 Ontology evolution process.
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ONTOLOGY EVOLUTION: STATE-OF-THE-ART
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(1) change capturing, (2) change representation, (3) semantics of change, (4) change implementation, (5) change propagation, and (6) change validation. In the following, we will use this evolution process as the basis for an analysis of the state-of-the-art.
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4.2.1. Change Capturing
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The process of ontology evolution starts with capturing changes either from explicit requirements or from the result of change discovery methods, which induce changes from patterns in data and usage. Explicit requirements are generated, for example, by ontology engineers who want to adapt the ontology to new requirements or by the end-users who provide the explicit feedback about the usability of ontology entities. The changes resulting from such requirements are called top-down changes. Implicit requirements leading to so-called bottom-up changes are re ected in the behavior of the system and can be discovered only through the analysis of this behavior. Stojanovic (2004) de nes different types of change discovery, we put in this work a focus on usage-driven and data-driven change discovery. Usage-driven changes result from the usage patterns created over a period of time. Once ontologies reach certain levels of size and complexity, the decision about which parts remain relevant and which are outdated is a huge task for ontology engineers. Usage patterns of ontologies and their metadata allow the detection of often or less often used parts, thus re ecting the interests of users in parts of ontologies. They can be derived by tracking querying and browsing behaviors of users during the application of ontologies as shown in Stojanovic et al. (2003b). Stojanovic (2004) de nes data-driven change discovery as the problem of deriving ontological changes from the ontology instances by applying techniques such as data-mining, Formal Concept Analysis (FCA) or various heuristics. For example, one possible heuristic might be: if no instance of a concept C uses any of the properties de ned for C, but only properties inherited from the parent concept, C is not necessary. An implementation of this notion of data-driven change discovery is included in the KAON tool suite (Maedche et al., 2003). Here we use a more general de nition of data-driven change discovery based on the assumption that an ontology is often learned or constructed in order to re ect the knowledge more or less implicitly given by a number of documents or a database. Therefore, any change to the underlying data set, such as a newly added document or a changed database entry, might require an update of the ontology. Data-driven change discovery can be de ned as the task of deriving ontology changes from modi cations to the knowledge from which the ontology has been constructed. One difference between these two de nitions is that the
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ONTOLOGY EVOLUTION
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latter always assumes an existing ontology, while the former can be applied to an empty ontology as well, but requires an evolving data set associated with this ontology. Ontology engineering follows well-established processes such as described by Sure et al. (2002a). So far, one has distinguished between manual and (semi-)automatic approaches to ontology engineering. If the ontology creation process is done manually, for example by a knowledge engineer in collaboration with domain experts supported by an ontology engineering system such as OntoEdit (Sure et al., 2002b), then both general and concrete relationships need to be held in the mind of this knowledge engineer. This requires a signi cant manual effort for codifying knowledge into ontologies. On the other hand, if the process of creating the ontology is done semi- or fully automatically with the help of an ontology learning system such as Text2Onto (Cimiano and Volker, 2005) these general and concrete relationships are generated and represented explicitly by the system. Of course, the rst kind of knowledge is always given by the speci c implementation of the ontology learning algorithms which are used. However, in order to enable an existing ontology learning system to support data-driven change discovery, it is necessary to make it store all available knowledge about concrete relationships between ontology entities and the data set.
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