Most search engines do their text query and retrieval based on keyword phrases. However, publishers cannot anticipate all possible ways in which users search for the items in their documents. In fact, many times, there may be no direct keyword match between a search phrase and descriptions of items that are perfect "hits" for the search. We present a highly automated solution to the problem of bridging the semantic gap between item information and search phrases. Our system can learn rule-based definitions that can be ascribed to search phrases with dynamic connotations by extracting structured item information from product catalogs and by utilizing a frequent itemset mining algorithm. We present experimental results for a realistic e-commerce domain. Also, we compare our rule-mining approach to vector-based relevance feedback retrieval techniques and show that our system yields definitions that are easier to validate and perform better.