Exploiting Background Knowledge when Learning Similarity Measures

The definition of similarity measures—one core component of each CBR application—leads to a serious knowledge acquisition problem if domain and application specific requirements have to be considered. To reduce the knowledge acquisition effort, different machine learning techniques have been developed in the past. In this paper, enhancements of our framework for learning knowledge-intensive similarity measures are presented. The described techniques aim on a restriction of the search space to be considered by the learning algorithm by exploiting available background knowledge.