Discovery Process in a B2B eMarketplace: A Semantic Matchmaking Approach

In the context of a customer-oriented value chain, companies must effectively address customers changing information needs during the process of acquiring a product or service to remain competitive. The ultimate goal of semantic matchmaking is to identify the best resources (supply) that fully meet the requirements (demand); however, such a goal is very difficult to achieve due to information distributed over disparate systems. To alleviate this problem in the context of eMarketplaces, the authors suggest an agent-enabled infomediary-based eMarketplace that enables semantic matchmaking. They extend and apply the exact, partial, and potential match algorithms developed in Di Noia et al. (2004) to show how partial and potential matches can become full matches. Specifically, the authors show how multi-criteria decision making techniques can be utilized to rank matches. They describe mechanisms for knowledge representation and exchange to allow partner organizations to seamlessly share information and knowledge to facilitate the discovery process in an eMarketplace context.