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A Review of Open Information Extraction Techniques

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Last updated: 24 Dec 2024

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Abstract

Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level. Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.Keywords—Open Information Extraction; Natural Language Processing

DOI

10.21608/ijci.2019.35099

Keywords

Open Information Extraction, natural language processing

Authors

First Name

Sally

Last Name

Ali

MiddleName

-

Affiliation

Dept. of Computer Science, Faculty of Computers and Information Menoufia University, Egypt

Email

smbm222@yahoo.com

City

-

Orcid

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First Name

Hamdy

Last Name

Mousa

MiddleName

-

Affiliation

Faculty of Computer and Information Menoufia University

Email

hamdimmm@hotmail.com

City

-

Orcid

0000-0001-9503-9124

First Name

M.

Last Name

Hussien

MiddleName

-

Affiliation

Dept. of Computer Science, Faculty of Computers and Information, Menoufia University, Egypt

Email

fci_3mh@yahoo.com

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-

Orcid

-

Volume

6

Article Issue

1

Related Issue

5795

Issue Date

2019-01-01

Receive Date

2018-09-16

Publish Date

2019-01-01

Page Start

20

Page End

28

Print ISSN

1687-7853

Online ISSN

2735-3257

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https://ijci.journals.ekb.eg/article_35099.html

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https://ijci.journals.ekb.eg/service?article_code=35099

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3

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Original Article

Type Code

877

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Journal

Publication Title

IJCI. International Journal of Computers and Information

Publication Link

https://ijci.journals.ekb.eg/

MainTitle

A Review of Open Information Extraction Techniques

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Article

Created At

22 Jan 2023