An approach to OIE designed for maximum speed is exemplified by the TextRunner system (Banko et al. 2007).  TextRunner is extraction pattern-based, but rather than employing patterns tuned to a few selected relations, it uses a range of patterns obtained automatically from a syntactically parsed training corpus, weighted via Bayesian machine learning methods to extract miscellaneous relations sentence-by-sentence from text. A rather different approach, termed “open knowledge extraction” (OKE), derives logical forms from parsed sentences, and simplifies and abstracts these so that they will tend to reflect general properties of the world. This is exemplified by the Knext system (KNowledge EXtraction from Text) (e.g., Schubert and Tong 2003). For example, the sentence  “I read a very informative book about China” allows KNEXT to abstract “factoids” to the effect that a person may occasionally read a book, and that books may occasionally be informative, and may occasionally be about a country. (Note that the specific references to the speaker and China have been abstracted to classes.) Another interesting development has been the extraction of script-like sequences of relations from large corpora by collocational methods (see Chambers and Jurafsky 2009). For example, the numerous newswire reports about arrest and prosecution of criminals can be mined to abstract typical event types involved, in chronological order, such as arrest, arraignment, plea, trial, and so on. A difficulty in all of this work is that most of the knowledge obtained is too ambiguously and incompletely formulated to provide a basis for inference chaining (but see for example Van Durme et al. 2009; Gordon and Schubert 2010; Schoenmackers et al. 2010).