Matching network of ontologies example adapted from [10].

Contexts in source publication

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... a set of ontologies, if the pairwise matching is applied to all ontologies from this set, it will sequentially compute the alignment of each pair of ontologies from the set. For example, given a set of ontologies =< <, , >, in which = {O 1 , O 2 , O 3 } of Figure 1, the pairwise matching of all ontologies in the set is obtained by computing ( ...

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... a set of ontologies, if the holistic matching is applied to all ontologies from this set, it will compute at once the alignment of all ontologies from the set. For example, given a set of ontologies =< <, , >, in which = {O 1 , O 2 , O 3 } of Figure 1, the holistic matching is obtained by computing ...

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... , n }, the network matching problem searches for a final network of ontologies f resulting from the alignments of the networks in . For instance, Figure 1 depicts two networks of ontologies ( and , each one with 3 ontologies ( = {o 1 , o 2 , o 3 } and = {o 1 , o 2 , o 3 }, describing two Systemsof-Systems. The goal is to match these two networks, finding all the alignments between them. ...

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... they are excluded from the final alignment result and the structural metrics, gathered from the graphs, might be harmed. In figure 1 Markov chains are stochastic problems that can be used to represent complex systems impacted by aleatory changes. Markov property states that the current state depends solely on the state before. ...

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... results were sent to the apriori algorithm in sequence. Figure 10 shows the comparison of the number of hits with the baseline 1 results. The figure shows one of the bests setups considering the recall in the table 6, since we will send those to the next phase. ...

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... due to the nature of the experiments and the need to track each intermediate result, manually checking them, our most extensive network had ten nodes, five each network. Figure 12 shows the experiment with force brute (All) uses the matcher (blue bar -LogMap) intensively to compute all the possible alignments. While the interception and union operation can be run in advance, both also can be run in parallel. ...

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... running the FIS with the semantic check, all the semantic checks can be run in parallel. Considering this particular case, we eliminated the union and the RW+FIS to verify how is the behavior of the execution time ( figure 13). Considering the experiment 2 × 2, it is notable the force brute (All) had the second-best time and Tables 9 and 8 shows the processing time from some experiments. ...

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... = Purple experiment running some steps in parallel. (Figure 14). comparisons (comparisonsAll), and the total execution time (LogAll or AlinAll). ...

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... 36, networksNodes = 12). Figure 14 shows the graphical comparison with the predicted execution times and the number of nodes. Table 10 shows the predicted processing time for experiments varying from the 6 × 6 until 30 × 30 experiment. ...

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... side effect is a bigger set of candidates improving the recall but harming the precision. Figure 12 show the execution time (average) for each operation. The RW+FIS execution time is in orange. ...

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... this limits our proposal, forcing the performance of one more operation to come back to the pairwise comparison. Thus, as soon the matchers can surpass that limitation, the proposed approach will be even better as we can observe comparing the LogMap contribution to the execution time (in light blue) in all experiments SubRW x SubRWPair ( figure: 12). Even though the pairwise final operation (in fact, a projection) does not harm the overall execution time, the matcher needs to return to the brute force approach, at least with smaller (pruned) ontologies. ...