MAPSOM: User Involvement in Ontology Matching

  • Václav Jirkovský
  • Ryutaro Ichise
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8388)


This paper presents a semi-automatic similarity aggregating system for ontology matching problem. The system consists of two main parts. The first part is aggregation of similarity measures with the help of self-organizing map. The second part incorporates user feedback for refining self-organizing map outcomes. The system calculates different similarity measures (e.g., string-based similarity measure, WordNet-based similarity measure...) to cover different causes of semantic heterogeneity. The next step is similarity aggregation by means of the self-organizing map and the ward clustering. The final step is the active learning phase for results tuning. We implemented this idea as MAPSOM framework. Our experimental results show that MAPSOM framework can be used for problems where the highest precision is needed.


Similarity Measure Active Learning User Involvement Ontology Match Triangular Norm 
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.



This research has been supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS12/188/OHK3/3T/13.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Czech Technical University in PraguePragueCzech Republic
  2. 2.Rockwell Automation Research and Development CenterPragueCzech Republic
  3. 3.National Institute of InformaticsTokyoJapan

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