---
title: "Open Information Extraction Systems and Downstream Applications"
authors:
  - "Mausam"
doi: ""
url: "https://ijcai.org/proceedings/2016/604"
pdf: "https://ijcai.org/proceedings/2016/0604.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "4074-4076"
track: ""
keywords:
---

# Open Information Extraction Systems and Downstream Applications

Mausam

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 4074-4076.

## Abstract

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.