This paper describes a method for learning which relations are highly associated with a given seed relation such as marriage or working for a company. Relation instances taken from a large knowledge base are used as seeds for obtaining candidate sentences expressing the associated relations. Relations of interest are identified by parsing the sentences and extracting dependency graph fragments, which are then ranked to determine which of them are most closely associated with the seed relation. We call the sets of associated relations relation theories. The quality of the induced theories is evaluated using human judgements.