Search engines are increasingly relying on structured data to provide direct answers to certain types of queries. However, extracting such structured data from text is challenging, especially due to the scarcity of explicitly expressed knowledge. Even when relying on large document collections, pattern-based information extraction approaches typically expose only insufficient amounts of information. This paper evaluates to what extent n-gram statistics, derived from volumes of texts several orders of magnitude larger than typical corpora, can allow us to overcome this bottleneck. An extensive experimental evaluation is provided for three different binary relations, comparing different sources of n-gram data as well as different learning algorithms.