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ONTOLOGY MEDIATION, MERGING, AND ALIGNING
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PROMPT the merged ontology replaces the source ontologies. The output of the merge operation in OntoMerge is not a complete merged ontology, as in PROMPT, but a bridge ontology which imports the source ontologies and which has a number of Bridging Axioms (see Figure 6.2(b)), which are translation rules used to connect the overlapping part of the source ontologies. The two source ontologies, together with the bridging axioms, are then treated as a single theory by a theorem prover optimized for three main operations: 1. Dataset translation (cf. instance transformation in de Bruijn and Polleres (2004)): dataset translation is the problem of translating a set of data (instances) from one representation to the other. 2. Ontology extension generation: the problem of ontology extension generation is the problem of generating an extension (instance data) O2s, given two related ontologies O1 and O2, and an extension O1s of ontology O1. The example given by the authors is to generate a WSDL extension based on an OWL-S description of the corresponding Web Service. 3. Querying different ontologies: query rewriting is a technique for solving the problem of querying different ontologies, whereas the authors of Dou et al. (2002) merely stipulate the problem.
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6.3. MAPPING AND QUERYING DISPARATE KNOWLEDGE BASES
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In the previous section we have seen an overview of a number of representative approaches for different aspects of ontology mediation in the areas of ontology mapping, alignment, and merging. In this section we focus on an approach for ontology mapping and ontology alignment to query disparate knowledge bases in a knowledge management scenario. However, the techniques are largely applicable to any ontology mapping or alignment scenario. In the area of knowledge management we assume there are two main tasks to be performed with ontology mappings: (a) transforming data between different representations, when transferring data from one knowledge base to another; and (b) querying of several heterogeneous knowledge bases, which have different ontologies. The ontologies in the area of knowledge management are large, but lightweight, that is, there is a concept hierarchy with many concepts, but there are relatively few relations and axioms in the ontology. From this follows that the mappings between the ontologies will be large as well, and they will generally be lightweight; the mapping will consist mostly of simple correspondence between concepts. The mappings between ontologies are not required to be completely accurate, because of the nature of the
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MAPPING AND QUERYING DISPARATE
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application of knowledge management: if a search result is inaccurate it is simply discarded by the user. In order to achieve ontology mapping, one needs to specify the relationship between the ontologies using some language. A natural candidate to express these relationships would seem to be the ontology language which is used for the ontologies themselves. We see a number of disadvantages to this approach:  Ontology language: there exist several different ontology languages for different purposes (e.g., RDFS (Brickley and Guha, 2004), OWL (Dean and Schreiber, 2004), WSML (de Bruijn et al., 2005)), and it is not immediately clear how to map between ontologies which are speci ed using different languages.  Independence of mapping: using an existing ontology language would typically require to import one ontology into the other, and specify the relationships between the concepts and relations in the resulting ontology; this is actually a form of ontology merging. The general disadvantage of this approach is that the mapping is tightly coupled with the ontologies; one can essentially not separate the mapping from the ontologies.  Epistemological adequacy: The constructs in an ontology language have not been de ned for the purpose of specifying mappings between ontologies. For example, in order to specify the correspondence between two concepts Human and Person in two ontologies, one could use some equivalence or subclass construct in the ontology language, even though the intension of the concepts in both ontologies is different. In Section 6.3.1 we describe a mapping language which is independent from the speci c ontology language but which can be grounded in an ontology language for some speci c tasks. The mapping language itself is based on a set of elementary mapping patterns which represent the elementary kinds of correspondences one can specify between two ontologies. As we have seen in Section 6.2.3, there exist many different alignment algorithms for the discovery of correspondences between ontologies. In Section 6.3.2 we present an interactive process for ontology alignment which allows to plug in any existing alignment algorithm. The input of this process consists of the ontologies which are to be mapped and the output is an ontology mapping. Writing mapping statements directly in the mapping language is a tedious and error-prone process. The mapping tool OntoMap is a graphical tool for creating ontology mappings. This tool described in Section 6.3.3 can be used to create a mapping between two ontologies from scratch or it can be used for the re nement of automatically discovered mappings.
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