Open Information Extraction Systems and Downstream Applications

Open Information Extraction (Open IE) extracts textual tuples comprising relation phrases and argument phrases from within a sentence, without requiring a pre-specified relation vocabulary. In this paper we first describe a decade of our progress on building Open IE extractors, which results in our latest extractor, OPENIE4, which is computationally efficient, outputs n-ary and nested relations, and also outputs relations mediated by nouns in addition to verbs. We also identify several strengths of the Open IE paradigm, which enable it to be a useful intermediate structure for end tasks. We survey its use in both human-facing applications and downstream NLP tasks, including event schema induction, sentence similarity, text comprehension, learning word vector embeddings, and more.