Automated product taxonomy mapping in an e-commerce environment. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Aanen, Steven S.; Vandic, Damir; Frasincar, Flavius
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1298
• Page End: 1313
• Publication Date: 2015-02-15
• Subjects: Products -- Semantic Web -- Schema -- Ontology -- Matching -- Mapping -- Merging -- E-commerce -- Web shop; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.032
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose an algorithm for automatic product taxonomy mapping in e-commerce. The algorithm uses word sense disambiguation techniques to handle heterogeneity. Our algorithm copes with composite categories in product category names. We compute path-similarities using lexical relatedness and structural information. Using real-world data, we show that we improve existing state-of-the-art methods. Abstract: Over the last few years, we have experienced a steady growth in e-commerce. This growth introduces many problems for services that want to aggregate product information and offerings. One of the problems that aggregation services face is the matching of product categories from different Web shops. This paper proposes an algorithm to perform this task automatically, making it possible to aggregate product information from multiple Web sites, in order to deploy it for search, comparison, or recommender systems applications. The algorithm uses word sense disambiguation techniques to address varying denominations between different taxonomies. Path similarity is assessed between source and candidate target categories, based on lexical relatedness and structural information. The main focus of the proposed solution is to improve the disambiguation procedure in comparison to an existing state-of-the-art approach, while coping with product taxonomy-specific characteristics, like composite categories, and re-examining lexical similarity and similarity aggregation in this context. The performance evaluation based on data from three real-world Web shops demonstrates that the proposed algorithm improves the benchmarked approach by 62% on average F 1 -measure.