Open Information Extraction from the Web

@inproceedings{Banko2008OpenIE,
  title={Open Information Extraction from the Web},
  author={Michele Banko and Michael J. Cafarella and Stephen Soderland and M. Alexander Broadhead and Oren Etzioni},
  booktitle={CACM},
  year={2008}
}
Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction… CONTINUE READING

Figures, Tables, and Topics from this paper.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.
SHOWING 1-10 OF 1,426 CITATIONS

An Open Information Extraction For Question Answering System

  • 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP)
  • 2018
VIEW 5 EXCERPTS
HIGHLY INFLUENCED

Knowledge Representation and Reasoning with Deep Neural Networks

VIEW 8 EXCERPTS
CITES RESULTS & BACKGROUND
HIGHLY INFLUENCED

VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2015
VIEW 7 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Knowledge-based graph document modeling

VIEW 13 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

KnowRob: A knowledge processing infrastructure for cognition-enabled robots

  • I. J. Robotics Res.
  • 2013
VIEW 8 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2006
2019

CITATION STATISTICS

  • 277 Highly Influenced Citations

  • Averaged 124 Citations per year from 2017 through 2019