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ONTOLOGY MEDIATION, MERGING, AND ALIGNING
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6.3.2. A (Semi-)Automatic Process for Ontology Alignment
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Creating mappings between ontologies is a tedious process, especially if the ontologies are very large. We introduce a semi-automatic alignment process implemented in the Framework for Ontology Alignment and Mapping (FOAM)-tool,2 which relieves the user of some of the burdens in creating mappings. It subsumes all the alignment approaches we are aware of (e.g., PROMPT (Noy and Musen, 2003), GLUE (Doan et al., 2003), QOM (Ehrig and Staab 2004; Ehrig and Sure 2004)). The input of the process consists of two ontologies which are to be aligned; the output is a set of correspondences between entities in the ontologies. Figure 6.3 illustrates its six main steps. 1. Feature engineering: it selects only parts of an ontology de nition in order to describe a speci c entity. For instance, alignment of entities may be based only on a subset of all RDFS primitives in the ontology. A feature may be as simple as the label of an entity, or it may include intentional structural descriptions such as super- or sub-concepts for concepts (a sports car being a subconcept of car), or domain and range for relations. Instance features may be instantiated attributes. Further, we use extensional descriptions. In an example we have fragments of two different ontologies, one describing the instance Daimler and one describing Mercedes. Both o1:Daimler and o2:Mercedes have a generic ontology feature called type. The values of this feature are automobile and luxury, and automobile, respectively. 2. Selection of next search steps: next, the derivation of ontology alignments takes place in a search space of candidate pairs. This step may choose to compute the similarity of a restricted subset of candidate concepts pairs of the two ontologies and to ignore others. For the running example we simply select every possible entity pair as an alignment candidate. In our example this means we will continue the comparison of o1:Daimler and o2:Mercedes. The QOM approach of Section 6.2.3 carries out a more ef cient selection.
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Iteration
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Similarity Aggregation
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Feature Engineering
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Figure 6.3 Alignment process.
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http://www.aifb.uni-karlsruhe.de/WBS/meh/foam
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MAPPING AND QUERYING DISPARATE
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Table 6.2 Comparing Entities Instances
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Feature/similarity assessment. No. FS1 FS2 Feature QF (label, X1 ) (parent, X1 ) Similarity QS string similarity (X1 ; X2 ) set equality (X1 ; X2 )
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3. Similarity assessment: it determines similarity values of candidate pairs. We need heuristic ways for comparing objects, that is similarity functions such as on strings, object sets, checks for inclusion, or inequality, rather than exact logical identity. In our example we use a similarity function based on the instantiated results, that is we check whether the two concept sets, parent concepts of o1:Daimler (automobile and luxury), and parent concepts of o2:Mercedes (only automobile) are the same. In the given case this is true to a certain degree, effectively returning a similarity value of 0.5. The corresponding feature/similarity assessment (FS2) is represented in Table 6.2 together with a second feature/similarity assessment (FS1) based on the similarity of labels. 4. Similarity aggregation: in general, there may be several similarity values for a candidate pair of entities from two ontologies, for example one for the similarity of their labels and one for the similarity of their relationship to other terms. These different similarity values for one candidate pair must be aggregated into a single aggregated similarity value. This may be achieved through a simple averaging step, but also through complex aggregation functions using weighting schemes. For example, we only have to result of the parent concept comparison which leads to: simil(o1:Daimler,o2:Mercedes) 0.5. 5. Interpretation: it uses the aggregated similarity values to align entities. Some mechanisms here are, for example to use thresholds for similarity (Noy and Musen, 2003), to perform relaxation labeling (Doan et al., 2003), or to combine structural and similarity criteria. simil(o1:Daimler,o2:Mercedes) 0.5 ! 0.5 leads to align(o1:Daimler) o2:Mercedes. This step is often also referred to as matcher. Semi-automatic approaches may present the entities and the alignment con dence to the user and let the user decide. 6. Iteration: several algorithms perform an iteration (see also similarity ooding (Melnik et al., 2002)) over the whole process in order to bootstrap the amount of structural knowledge. Iteration may stop when no new alignments are proposed, or if a prede ned number of iterations has been reached. Note that in a subsequent iteration one or several of steps 1 through 5 may be skipped, because all features might already be available in the appropriate format or because some similarity computation might only be required in the rst round. We use the intermediate results of step 5 and feed them again into the process and stop after a prede ned number of iterations. The output of the alignment process is a mapping between the two input ontologies. We cannot in general assume that all mappings
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