Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy

Qinshen Wang; Yinan An; Yaping Li; Hongzhi Wang

doi:10.1109/ICPDS47662.2019.9017169

Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy

Qinshen Wang
, Yinan An
, Yaping Li
, Hongzhi Wang

Faculty of Computing

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

As the significance of knowledge base has been widely accepted during the past decade, how to efficiently eliminate the noises in the knowledge base becomes a key problem since the automatically constructed knowledge base usually contains lots of noises that disturbs its application. Based on the observation for Is-a relations that the real entities of a concept A always share several same ancestors besides A, we come up with an Is-a relations noise elimination approach. In this paper, we will elaborate on this approach and explain the pseudocode of it. Our experimental results demonstrate that such an approach is capable of eliminating the noises in the knowledge base efficiently.

Original language	English
Title of host publication	2019 IEEE International Conference on Power Data Science, ICPDS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	101-104
Number of pages	4
ISBN (Electronic)	9781728137759
DOIs	https://doi.org/10.1109/ICPDS47662.2019.9017169
State	Published - Nov 2019
Event	2019 IEEE International Conference on Power Data Science, ICPDS 2019 - Taizhou, China Duration: 22 Nov 2019 → 24 Nov 2019

Publication series

Name	2019 IEEE International Conference on Power Data Science, ICPDS 2019

Conference

Conference	2019 IEEE International Conference on Power Data Science, ICPDS 2019
Country/Territory	China
City	Taizhou
Period	22/11/19 → 24/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

knowledge engineering
knowledge representation
semantic web

Access to Document

10.1109/ICPDS47662.2019.9017169

Cite this

Wang, Q., An, Y., Li, Y., & Wang, H. (2019). Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy. In 2019 IEEE International Conference on Power Data Science, ICPDS 2019 (pp. 101-104). Article 9017169 (2019 IEEE International Conference on Power Data Science, ICPDS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPDS47662.2019.9017169

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title = "Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy",

abstract = "As the significance of knowledge base has been widely accepted during the past decade, how to efficiently eliminate the noises in the knowledge base becomes a key problem since the automatically constructed knowledge base usually contains lots of noises that disturbs its application. Based on the observation for Is-a relations that the real entities of a concept A always share several same ancestors besides A, we come up with an Is-a relations noise elimination approach. In this paper, we will elaborate on this approach and explain the pseudocode of it. Our experimental results demonstrate that such an approach is capable of eliminating the noises in the knowledge base efficiently.",

keywords = "knowledge engineering, knowledge representation, semantic web",

author = "Qinshen Wang and Yinan An and Yaping Li and Hongzhi Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Power Data Science, ICPDS 2019 ; Conference date: 22-11-2019 Through 24-11-2019",

year = "2019",

month = nov,

doi = "10.1109/ICPDS47662.2019.9017169",

language = "英语",

series = "2019 IEEE International Conference on Power Data Science, ICPDS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "101--104",

booktitle = "2019 IEEE International Conference on Power Data Science, ICPDS 2019",

address = "美国",

}

Wang, Q, An, Y, Li, Y & Wang, H 2019, Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy. in 2019 IEEE International Conference on Power Data Science, ICPDS 2019., 9017169, 2019 IEEE International Conference on Power Data Science, ICPDS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 101-104, 2019 IEEE International Conference on Power Data Science, ICPDS 2019, Taizhou, China, 22/11/19. https://doi.org/10.1109/ICPDS47662.2019.9017169

Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy. / Wang, Qinshen; An, Yinan; Li, Yaping et al.
2019 IEEE International Conference on Power Data Science, ICPDS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 101-104 9017169 (2019 IEEE International Conference on Power Data Science, ICPDS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Li, Yaping

AU - Wang, Hongzhi

PY - 2019/11

Y1 - 2019/11

N2 - As the significance of knowledge base has been widely accepted during the past decade, how to efficiently eliminate the noises in the knowledge base becomes a key problem since the automatically constructed knowledge base usually contains lots of noises that disturbs its application. Based on the observation for Is-a relations that the real entities of a concept A always share several same ancestors besides A, we come up with an Is-a relations noise elimination approach. In this paper, we will elaborate on this approach and explain the pseudocode of it. Our experimental results demonstrate that such an approach is capable of eliminating the noises in the knowledge base efficiently.

AB - As the significance of knowledge base has been widely accepted during the past decade, how to efficiently eliminate the noises in the knowledge base becomes a key problem since the automatically constructed knowledge base usually contains lots of noises that disturbs its application. Based on the observation for Is-a relations that the real entities of a concept A always share several same ancestors besides A, we come up with an Is-a relations noise elimination approach. In this paper, we will elaborate on this approach and explain the pseudocode of it. Our experimental results demonstrate that such an approach is capable of eliminating the noises in the knowledge base efficiently.

KW - knowledge engineering

KW - knowledge representation

KW - semantic web

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U2 - 10.1109/ICPDS47662.2019.9017169

DO - 10.1109/ICPDS47662.2019.9017169

M3 - 会议稿件

AN - SCOPUS:85082500574

T3 - 2019 IEEE International Conference on Power Data Science, ICPDS 2019

SP - 101

EP - 104

BT - 2019 IEEE International Conference on Power Data Science, ICPDS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Power Data Science, ICPDS 2019

Y2 - 22 November 2019 through 24 November 2019

ER -

Wang Q, An Y, Li Y, Wang H. Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy. In 2019 IEEE International Conference on Power Data Science, ICPDS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 101-104. 9017169. (2019 IEEE International Conference on Power Data Science, ICPDS 2019). doi: 10.1109/ICPDS47662.2019.9017169

Set-based Noise Elimination for Is-a Relations in a Large-Scale Lexical Taxonomy

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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