We study Refer-to-as relations as a new type of semantic knowledge. Compared to the much studied is-A relation, which concerns factual taxonomic knowledge, Refer-to-as relations aim to address pragmatic semantic knowledge. For example, a "penguin" is a "bird" from a taxonomic point of view, but people rarely refer to a "penguin" as a "bird" in vernacular use. This observation closely relates to the entry-level categorization studied in Psychology. We posit that Refer-to-as relations can be learned from data, and that both textual and visual information would be helpful in inferring the relations. By integrating existing lexical structure knowledge with language statistics and visual similarities, we formulate a collective inference approach to map all object names in an encyclopedia to commonly used names for each object. Our contributions include a new labeled data set, the collective inference and optimization approach, and the computed mappings and similarities.