levenshteinDistance.java

package plugins.HarmonizationComponent;

import java.io.File;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;

import jxl.Workbook;
import jxl.write.Label;
import jxl.write.WritableSheet;
import jxl.write.WritableWorkbook;

import org.semanticweb.owlapi.model.IRI;

public class levenshteinDistance
{

	private OWLFunction owlFunction = null;

	private double cutOff = 60.0;

	public static void main(String args[]) throws Exception
	{

		System.out.println("The mapping has started");

		levenshteinDistance test = new levenshteinDistance();

		test.startMatching();

		System.out.println("The mapping has been done!");
	}

	public void startMatching() throws Exception
	{

		WritableWorkbook workbook = Workbook.createWorkbook(new File(
				"/Users/pc_iverson/Desktop/Ontology_term_pilot/result2.xls"));

		WritableSheet sheet = workbook.createSheet("result", 0);

		LevenshteinDistanceModel matchingModel = new LevenshteinDistanceModel();

		String fileName = "/Users/pc_iverson/Desktop/Ontology_term_pilot/LeidseHPOLijste2.xls";

		tableModel model = new tableModel(fileName, false);

		List<String> originalTerms = model.getColumn("Symptom");

		String ontologyFileName = "/Users/pc_iverson/Desktop/Ontology_term_pilot/human-phenotype-ontology.obo";

		this.owlFunction = new OWLFunction(ontologyFileName);

		owlFunction.labelMapURI();

		List<String> listOfOntologyTerms = owlFunction.getAllTerms();

		String file2 = "/Users/pc_iverson/Desktop/Ontology_term_pilot/CineasDiagnoses.xls";

		tableModel model2 = new tableModel(file2, false);

		List<String> listOfSympotoms = model2.getColumn("ZIEKTETEKST");

		int rowIndex = 0;

		for (String eachTerm : originalTerms)
		{

			double bestScore = 0;

			String matchedOntologyTerm = "";

			String synonymForOntologyTerm = "";

			Map<String, Double> candidateMatching = new HashMap<String, Double>();

			Map<String, String> candidateHPOId = new HashMap<String, String>();

			for (String ontologyTerm : listOfOntologyTerms)
			{

				double similarity = matchingModel.stringMatching(eachTerm, ontologyTerm, false);

				if (bestScore < similarity)
				{
					bestScore = similarity;
					matchedOntologyTerm = ontologyTerm;
					synonymForOntologyTerm = "";
				}

				if (similarity > cutOff)
				{
					candidateMatching.put(ontologyTerm, similarity);
					candidateHPOId.put(ontologyTerm, owlFunction.getOntologyTermID(ontologyTerm));
				}

				List<String> synonyms = owlFunction.getAnnotation(
						ontologyTerm,
						IRI.create(owlFunction.getOntologyIRI()
								.subSequence(0, owlFunction.getOntologyIRI().length() - 2).toString()
								+ "synonym"));

				for (String eachSynonym : synonyms)
				{

					double similarity2 = matchingModel.stringMatching(eachTerm, eachSynonym, false);

					if (bestScore < similarity2)
					{
						bestScore = similarity2;
						synonymForOntologyTerm = eachSynonym;
						matchedOntologyTerm = ontologyTerm;
					}

					if (similarity2 > cutOff)
					{
						String output = "Synonym match: " + eachSynonym + "; The ontology term: " + ontologyTerm;
						candidateMatching.put(output, similarity2);
						candidateHPOId.put(output, owlFunction.getOntologyTermID(ontologyTerm));
					}
				}
			}

			System.out.println(eachTerm + "\t" + matchedOntologyTerm + "\t"
					+ owlFunction.getOntologyTermID(matchedOntologyTerm) + "\t" + bestScore);

			Label originalTermCell = new Label(0, rowIndex, eachTerm);

			Label termIDCell = new Label(2, rowIndex, owlFunction.getOntologyTermID(matchedOntologyTerm));

			if (!synonymForOntologyTerm.equals(""))
			{
				matchedOntologyTerm = "Synonym match: " + synonymForOntologyTerm + "; The ontology term: "
						+ matchedOntologyTerm;
			}

			Label ontologyTermCell = new Label(1, rowIndex, matchedOntologyTerm);

			Label scoreCell = new Label(3, rowIndex, "" + bestScore);

			sheet.addCell(originalTermCell);

			sheet.addCell(ontologyTermCell);

			sheet.addCell(termIDCell);

			sheet.addCell(scoreCell);

			// if(bestScore < 90){
			//
			// for(Entry<String, Double> eachEntry :
			// candidateMatching.entrySet()){
			//
			// String term = eachEntry.getKey();
			//
			// Double similarity = eachEntry.getValue();
			//
			// String HPOId = candidateHPOId.get(term);
			//
			// originalTermCell = new Label(0, rowIndex, eachTerm);
			//
			// ontologyTermCell = new Label(1, rowIndex, term);
			//
			// termIDCell = new Label(2, rowIndex, HPOId);
			//
			// scoreCell = new Label(3, rowIndex, "" + similarity);
			//
			// //sheet.addCell(originalTermCell);
			//
			// sheet.addCell(ontologyTermCell);
			//
			// sheet.addCell(termIDCell);
			//
			// sheet.addCell(scoreCell);
			//
			// rowIndex++;
			// }
			// }

			double bestScoreForSympotom = 0;

			String matchedSympotom = "";

			for (String sympotom : listOfSympotoms)
			{

				double similiarityScore = matchingModel.stringMatching(eachTerm, sympotom, false);

				if (bestScoreForSympotom < similiarityScore)
				{

					bestScoreForSympotom = similiarityScore;

					matchedSympotom = sympotom;

				}
			}

			Label symptomCell = new Label(4, rowIndex, matchedSympotom);

			Label scoreForSymptomCell = new Label(5, rowIndex, "" + bestScoreForSympotom);

			sheet.addCell(symptomCell);

			sheet.addCell(scoreForSymptomCell);

			rowIndex++;
		}

		workbook.write();
		workbook.close();
	}
}