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    STUDIA INFORMATICA - Issue no. 3 / 2010  
         
  Article:   ALIGNMENT OF CUSTOM STANDARDS BY MACHINE LEARNING ALGORITHMS.

Authors:  ALEXANDRINA ROGOZAN, LAURA DIOŞAN. 		 
       
         
  Abstract:  Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier’s hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++tool. The performance of our aligners is shown by the results obtained on the test set.

Key words and phrases. Concept alignment, Machine Learning, Binary Classification, Support Vector Machine.

  
         
     
         
         
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