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Optimal Confidence Determination

Many matching systems use varying confidences for each correspondence, typically in the range [0, 1]. Removing low-confidence matches can significantly improve precision and F1.

In MELT, ConfidenceFinder can be used to determine the optimal threshold given any ExecutionResult.

Note that it is much better performance-wise to optimize a matcher execution result that contains no removed correspondences rather than running a matcher multiple times with different cut-off points. Therefore, ConfidenceFinder works with an ExecutionResult instance rather than with a matcher instance. If you want to fine-tune parameters of an actual matching instance, use class GridSearch.

Example:

import de.uni_mannheim.informatik.dws.melt.matching_data.TrackRepository;
import de.uni_mannheim.informatik.dws.melt.matching_eval.ExecutionResult;
import de.uni_mannheim.informatik.dws.melt.matching_eval.ExecutionResultSet;
import de.uni_mannheim.informatik.dws.melt.matching_eval.Executor;
import de.uni_mannheim.informatik.dws.melt.matching_eval.paramtuning.ConfidenceFinder;
import de.uni_mannheim.informatik.dws.melt.matching_jena_matchers.external.matcher.SimpleStringMatcher;

public class ConfidenceFinderExample {

    
    public static void main(String[] args) {

        // let's run a default matcher on the OAEI anatomy track:
        ExecutionResultSet ers = Executor.run(TrackRepository.Anatomy.Default, new SimpleStringMatcher());
        for (ExecutionResult e : ers) {

            // the actual optimization:
            double bestConfidence = ConfidenceFinder.getBestConfidenceForFmeasure(e);

            // just some meaningful output:
            System.out.println("Best confidence for matcher " + e.getMatcherName() +
                    " on " + e.getTrack().getName() + " (" + e.getTestCase().getName() + "): " +
                    bestConfidence);
        }
    }

}

All correspondences with a confidence LOWER than the result should be discarded. You can do this by applying a filter in a matching pipeline. MELT provides ConfidenceFilter for exactly this case:

Example:

import de.uni_mannheim.informatik.dws.melt.matching_jena.MatcherPipelineYAAAJenaConstructor;
import de.uni_mannheim.informatik.dws.melt.matching_jena.MatcherYAAAJena;
import de.uni_mannheim.informatik.dws.melt.matching_jena_matchers.external.matcher.SimpleStringMatcher;
import de.uni_mannheim.informatik.dws.melt.matching_jena_matchers.filter.ConfidenceFilter;

public class ConfidenceFilterExample {


    public static void main(String[] args) {

        // assume that we determined an optimal confidence as outlined above
        double bestConfidence = 0.8;

        // build a matcher pipeline with the filter at the end:
        MatcherYAAAJena matcher = new MatcherPipelineYAAAJenaConstructor(
                new SimpleStringMatcher(), // some matcher
                new ConfidenceFilter(bestConfidence)); // let's filter the result using ConfidenceFilter

        // do something with the matcher :)
    }

}