Ying Ding, Maksym Korotkiy, Borys Omelayenko, Vera Kartseva, Volodymyr Zykov, Michel Klein, Ellen Schulten, and Dieter Fensel
Internet and Web technology already penetrates many aspects of our daily life and its importance as a medium for business transactions will grow exponentially during the next years. B2B market places provide new kinds of services to their clients. Simple 1-1 connections are being replaced by n-m relationships between customers and vendors. However, this new flexibility in electronic trading also generates serious challenges for the parties that want to realize it. The main problem here is caused by the heterogeneity of information descriptions used by vendors and customers. Intelligent solutions that help to mechanize the process of structuring, classifying, aligning, and personalizing are a key requisite for successfully overcoming the current bottlenecks in B2B electronic commerce. In this paper, we outline a system called GoldenBullet that applies techniques from information retrieval and machine learning to the problem of product data classification. The system helps to mechanize an important and labor-intensive task of content management for B2B Ecommerce.