No abstract

No abstract

No abstract

Abstract. This paper proposes SCHEMA, an algorithm for automated mapping between heterogeneous product taxonomies in the e-commerce domain. SCHEMA utilises word sense disambiguation techniques, based on the ideas from the algorithm proposed by Lesk, in combination with the semantic lexicon WordNet. For finding candidate map categories and determining the path-similarity we propose a node matching function that is based on the Levenshtein distance. The final mapping quality score is calculated using the Damerau-Levenshtein distance and a nodedissimilarity penalty. The performance of SCHEMA was tested on three real-life datasets and compared with PROMPT and the algorithm proposed by Park & Kim. It is shown that SCHEMA improves considerably on both recall and F1-score, while maintaining similar precision.

With the vast amount of information available on the Web, there is an increasing need to structure Web data in order to make it accessible to both users and machines. E-commerce is one of the areas in which growing data congestion on the Web has serious consequences. This paper proposes a framework that is capable of populating a product ontology using tabular product information from Web shops. By formalizing product information in this way, better product comparison or recommendation applications could be built. Our approach employs both lexical and syntactic matching for mapping properties and instantiating values. The performed evaluation shows that instantiating consumer electronics from Best Buy and Newegg.com results in an F1 score of approximately 77%.