This paper applies data mining techniques to extract retailing knowledge from the POS information provided by an inter-organizational information service center in Taiwan. Many mutually competitive retail chains sponsored the data warehouse. They must, of course, protect their secrets, while cooperating to mine the inter-organizational data and thereby extract macro-level knowledge about consumers' behavior. Many difficulties arise from this, because each transaction contains only a summary indicating the total sales of a single product in a store during a month and more detailed data are not available. Moreover, with many retail store chains cooperating, the meaning of the quantitative data, such as price and quantity, is difficult to compare and hard to interpret. No previous research addressed this problem. A series of steps were implemented to help solve this problem; they include defining semantic association rules (AR), transforming the quantitative data into semantic data and developing algorithms for mining the knowledge. Finally, we consolidated these ideas and implemented a prototype system.