Most search engines do their text query and retrieval using keywords. However, vendors cannot anticipate all possible ways in which shoppers search for their products. In fact, many times, there may be no direct keyword match between a search phrase and descriptions of products that are perfect “hits” for the search. A highly automated solution to the problem of bridging the semantic gap between product descriptions and search phrases used by Web shoppers is developed. By using scalable information extraction techniques from Web sources and a frequent itemset mining algorithm, our system can learn how meanings can be ascribed to popular search phrases with dynamic connotations. By annotating the product databases based on the meanings of search phrases mined by our system, catalog owners can boost the findability of their products.