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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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Figure 2.9 depicts the logical design of the integrated transcoder. The switches S1 and S2 control whether requantization or spatial downscaling is activated. If requantization is used, switches S1 and S2 will be connected to A1 and A2 respectively, otherwise they will be connected to B1 and B2 respectively to activate spatial downscaling. The following sections present in more detail the algorithms used in the integrated transcoder. 2.5.3.1 Drift Compensation Loop Both requantization and spatial downscaling introduce noises in the transcoded video frames. As MPEG-1 makes use of predictive coding for B frames and P frames, these noises can accumulate along frames in the same group of pictures (GOP), further degrading the visual quality in the predicted frames. To prevent this problem, known as drift, we can feed back the noises to the predictor to compensate for the prediction errors. This is known as drift compensation. For requantization, error is introduced during the requantization process where larger quantization step sizes are used. To compensate for the quantization errors, we can dequantize (Q2 1 ) the quantized DCT coef cients and compute the quantization error by comparing to the original DCT coef cients. The quantization error is then stored in the corresponding error





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To resolve changes, they have to be identi ed and represented in a suitable format which means that the change representation needs to be de ned for a given ontology model. Changes can be represented on various levels of granularity, for example as elementary or complex changes. The set of ontology change operations depends heavily on the underlying ontology model. Most existing work on ontology evolution builds on frame-like or object models, centred around classes, properties, etc. Stojanovic (2004) derives a set of ontology changes for the KAON ontology model. The author speci es ne-grained changes that can be performed in the course of the ontology evolution. They are called elementary changes, since they cannot be decomposed into simpler changes. An elementary change is either an add or remove transformation, applied to an entity in the ontology model. The author also mentions that this level of change representation is not always appropriate and therefore introduces the notion of composite changes: a composite change is an ontology change that modi es (creates, removes or changes) one and only one level of neighborhood of entities in the ontology, where the neighborhood is de ned via structural links between entities. Examples for such composite changes would be: Pull concept up, Copy Concept, Split Concept, etc. Further, the author introduces complex changes: a

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buffer for the macroblock, which is then extracted using MC-DCT [52] and added to the next predicted frame to compensate for the quantization error. For spatial downscaling, as error is introduced in both quantization and DCT operations, we need to perform both dequantization and inverse DCT to compute the aggregate errors. We can then incorporate these errors into the residual errors in the motion compensation process to prevent drifting. 2.5.3.2 Reconstruction of the Downscaled Motion Vectors In spatial downscaling, instead of performing motion estimation again on the downscaled frames, which is computationally expensive, we can use the AMVR algorithm proposed by Bo et al. [45] to reconstruct the motion vectors for the downscaled frame. In AMVR, the downscaled motion vector, denoted by MV , is computed from

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