An Iterative Algorithm for Ontology Mapping Capable of Using Training Data

  • Andreas Heß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4011)


We present a new iterative algorithm for ontology mapping where we combine standard string distance metrics with a structural similarity measure that is based on a vector representation. After all pairwise similarities between concepts have been calculated we apply well-known graph algorithms to obtain an optimal matching. Our algorithm is also capable of using existing mappings to a third ontology as training data to improve accuracy. We compare the performance of our algorithm with the performance of other alignment algorithms and show that our algorithm can compete well against the current state-of-the-art.


Bipartite Graph Pairwise Similarity Ontology Mapping Stable Marriage Hungarian Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Heß
    • 1
    • 2
  1. 1.Vrije Universiteit Amsterdam 
  2. 2.University College Dublin 

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