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Relation extraction has been a subject of significant study over the past decade. Most relation extractors have been developed by combining the training of complex computational systems on large volumes of annotations with extensive rule writing by language experts. Moreover, many relation extractors are reliant on other non-trivial NLP technologies which themselves are developed through significant human efforts, such as entity tagging, parsing, etc. Due to the high cost of creating and assembling the required resources, relation extractors have typically been developed for only high-resourced languages. In this paper, we describe a near-zero-cost methodology to build relation extractors for significantly distinct non-English languages using only freely available Wikipedia and other web documents, and some knowledge of English. We apply our methodology and build alma-mater, birthplace, father, occupation, and spouse relation extractors in Greek, Spanish, Russian, and Chinese. We conduct evaluations of induced relations at the file level which are the most refined we have seen in the literature.