While knowledge extraction using Hearst-like patterns is narrowly aimed at certain predetermined types of knowledge, other approaches are aimed at open information extraction (OIE).  These seek to discover a broad range of relational knowledge, in some cases including entailments (in a rather loose sense) between different relations. An early and quite successful system of this genre was Lin and Pantel's DIRT system (Discovery of Inference Rules from Text), which used collocational statistics to build up a database of “inference rules” (Lin and Pantel 2001). An example of a rule might be “X finds a solution to Y ≈ X solves Y.” The statistical techniques used included clustering of nominals into similarity groups based on their tendency to occur in the same argument positions of the same verbs, and finding similar relational phrases (such as “finds a solution to” and “solves”), based their tendency to connect the same, or similar, pairs of nominals. Many of the rules were later refined by addition of type constraints to the variables, obtained by abstracting from particular nominals via WordNet (Pantel et al.  2007).  