Locations are often expressed in physical coordinates such as an [X; Y ] tuple in some coordinate system. Unfortunately, a vast majority of location-based applications desire the semantic translation of coordinates, i.e., store-names like Starbucks, Macy's, Panera. Past work has mostly focused on achieving localization accuracy, while assuming that the translation of physical to semantic coordinates will be done manually. In this paper, we explore an opportunity for automatic semantic localization - the presence of a website corresponding to each physical store. We propose to correlate the information seen in a physical store with that found in websites of the stores around that location, to recognize that store. Specifically, we assume a repository of crowdsourced WiFi-tagged pictures from different stores. By correlating words inside the pictures, against words extracted from store websites, our proposed system can automatically label clusters of pictures, and the corresponding WiFi APs, with the store name. Later, when a user enters a store, her smartphone can scan the WiFi APs and consult a lookup table to recognize the store she is in. Our preliminary experiments with 18 stores in a shopping mall show that, our prototype system could correctly match the text from the physical stores with the text extracted from the corresponding web sites and hence label WiFi APs with store names with an accuracy upwards of 90%, which encourages us to pursue this study further. Moreover, we believe the core idea of correlating physical and web sites has broader applications beyond semantic localization, leading to better product placement and shopping experience, yielding benefits for both store owners and shoppers.