An Automated Approach to Product Taxonomy Mapping in E-Commerce

Due to the ever-growing amount of information available on Web shops, it has become increasingly difficult to get an overview of Web-based product information. There are clear indications that better search capabilities, such as the exploitation of annotated data, are needed to keep online shopping transparent for the user. For example, annotations can help present information from multiple sources in a uniform manner. This paper proposes an algorithm that can autonomously map heterogeneous product taxonomies for Web shop data integration purposes. The proposed approach uses word sense disambiguation techniques, approximate lexical matching, and a mechanism that deals with composite categories. Our algorithm's performance on three real-life datasets was compared favourably against two other state-of-the-art taxonomy mapping algorithms. The experiments show that our algorithm performs at least twice as good compared to the other algorithms w.r.t. precision and F-measure.