In this paper we propose SCHEMA, an algorithm that automatically maps heterogeneous product taxonomiesin the domain of e-commerce. SCHEMA employs a custom word sense disambiguation technique,based on the Lesk algorithm, in combination with the semantic lexicon WordNet. For findingcandidate target categories and determining the path-similarity we propose a semantic category matchingalgorithm that takes into account the disambiguation process of a category. The mapping quality score iscalculated using the Damerau-Levenshtein distance and a node-dissimilarity penalty. The performance ofSCHEMA was tested on three real-life datasets and compared to PROMPT and the algorithm proposedby Park & Kim. The comparison shows that SCHEMA improves considerably recall and F1-score, whilemaintaining similar precision.

Additional Metadata
Persistent URL
Conference 24th Benelux Conference on Artificial Intelligence, BNAIC 2012
Aanen, S.S, Nederstigt, L.J, Vandic, D, & Frasincar, F. (2012). Mapping product taxonomies in e-commerce. Presented at the 24th Benelux Conference on Artificial Intelligence, BNAIC 2012. Retrieved from