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.
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