Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework

摘要

This paper describes the learning of new similarity values for existing measures within the framework FEATURE-TAK. Maintenance of similarity measures is not easy, especially when having a semi-automated approach to relieve the knowledge engineer. Based on the extension of the vocabulary, the newly added values have to be integrated into the similarity measures with an initial similarity value to be useful. We describe the extension of the similarity measures with automated taxonomy extension and one-mode projections and present a comprehensive evaluation and comparison between the different approaches to highlight the advantages and short comings.