A LARGE SCALE TAXONOMY MAPPING EVALUATION

Abstract. Matching hierarchical structures, like taxonomies or web di-rectories, is the premise for enabling interoperability among heteroge-nous data organizations. While the number of new matching solutions is increasing the evaluation issue is still open. This work addresses the problem of comparison for pairwise matching solutions. A methodology is proposed to overcome the issue of scalability. A large scale dataset is developed based on real world case study namely, the web directories of Google, Looksmart and Yahoo!. Finally, an empirical evaluation is per-formed which compares the most representative solutions for taxonomy matching. We argue that the proposed dataset can play a key role in supporting the empirical analysis for the research effort in the area of taxonomy matching.