Our team had to tackle a case in which large text collections had to be searched efficiently. They case was particularly focused key concepts like Organizations, People, Locations, but also relations between them. ML methods are able to learn from already annotated examples how to extract relations expressed in text. These methods however need large amount of expert annotations, which are quite expensive.
The current case is an exploration of the idea whether an AI algorithm can be trained to infer the relations off a distant-labeled set, auto-generated via DBPedia.