Open Information Extraction with Global Structure Constraints

Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Frank F. Xu, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. However, current open IE systems ignore the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel open IE system, called ReMine, which integrates local context signal and global structural signal in a unified framework with distant supervision. The new system can be efficiently applied to different domains as it uses facts from external knowledge bases as supervision; and can effectively score sentence-level tuple extractions based on corpus-level statistics. Specifically, we design a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of sentence-level extractions with a translating-based objective. Experiments on real-world corpora from different domains demonstrate the effectiveness and robustness of ReMine when compared to other open IE systems.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages57-58
Number of pages2
ISBN (Electronic)9781450356404
DOIs
StatePublished - Apr 23 2018
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: Apr 23 2018Apr 27 2018

Publication series

NameThe Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
CountryFrance
CityLyon
Period4/23/184/27/18

Keywords

  • open information extraction
  • weakly-supervised learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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  • Cite this

    Zhu, Q., Ren, X., Shang, J., Zhang, Y., Xu, F. F., & Han, J. (2018). Open Information Extraction with Global Structure Constraints. In The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 (pp. 57-58). (The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018). Association for Computing Machinery, Inc. https://doi.org/10.1145/3184558.3186927