ARIA: AsymmetRy Resistant Instance Alignment

Sanghoon Lee; Seung Won Hwang

ARIA: AsymmetRy Resistant Instance Alignment

Sanghoon Lee, Seung Won Hwang

College of Computing

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

7 Citations (Scopus)

Abstract

We study the problem of instance alignment between knowledge bases (KBs). Existing approaches, exploiting the "symmetry" of structure and information across KBs, suffer in the presence of asymmetry, which is frequent as KBs are independently built. Specifically, we observe three types of asymmetries (in concepts, in features, and in structures). Our goal is to identify key techniques to reduce accuracy loss caused by each type of asymmetry, then design Asymmetry-Resistant Instance Alignment framework (ARIA). ARIA uses two-phased blocking methods considering concept and feature asymmetries, with a novel similarity measure over-coming structure asymmetry. Compared to a state-of-the-art method, ARIA increased precision by 19% and recall by 2%, and decreased processing time by more than 80% in matching large-scale real-life KBs.

Original language	English
Title of host publication	Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
Publisher	AI Access Foundation
Pages	94-100
Number of pages	7
ISBN (Electronic)	9781577356776
Publication status	Published - 2014
Event	28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada Duration: 2014 Jul 27 → 2014 Jul 31

Publication series

Name	Proceedings of the National Conference on Artificial Intelligence
Volume	1

Other

Other	28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Country/Territory	Canada
City	Quebec City
Period	14/7/27 → 14/7/31

Bibliographical note

Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence.

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

Lee, S., & Hwang, S. W. (2014). ARIA: AsymmetRy Resistant Instance Alignment. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence (pp. 94-100). (Proceedings of the National Conference on Artificial Intelligence; Vol. 1). AI Access Foundation.

Lee, Sanghoon ; Hwang, Seung Won. / ARIA : AsymmetRy Resistant Instance Alignment. Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence. AI Access Foundation, 2014. pp. 94-100 (Proceedings of the National Conference on Artificial Intelligence).

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abstract = "We study the problem of instance alignment between knowledge bases (KBs). Existing approaches, exploiting the {"}symmetry{"} of structure and information across KBs, suffer in the presence of asymmetry, which is frequent as KBs are independently built. Specifically, we observe three types of asymmetries (in concepts, in features, and in structures). Our goal is to identify key techniques to reduce accuracy loss caused by each type of asymmetry, then design Asymmetry-Resistant Instance Alignment framework (ARIA). ARIA uses two-phased blocking methods considering concept and feature asymmetries, with a novel similarity measure over-coming structure asymmetry. Compared to a state-of-the-art method, ARIA increased precision by 19% and recall by 2%, and decreased processing time by more than 80% in matching large-scale real-life KBs.",

author = "Sanghoon Lee and Hwang, {Seung Won}",

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language = "English",

series = "Proceedings of the National Conference on Artificial Intelligence",

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Lee, S & Hwang, SW 2014, ARIA: AsymmetRy Resistant Instance Alignment. in Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence. Proceedings of the National Conference on Artificial Intelligence, vol. 1, AI Access Foundation, pp. 94-100, 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, Quebec City, Canada, 14/7/27.

ARIA: AsymmetRy Resistant Instance Alignment. / Lee, Sanghoon; Hwang, Seung Won.
Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence. AI Access Foundation, 2014. p. 94-100 (Proceedings of the National Conference on Artificial Intelligence; Vol. 1).

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

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T2 - 28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014

AU - Lee, Sanghoon

AU - Hwang, Seung Won

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N2 - We study the problem of instance alignment between knowledge bases (KBs). Existing approaches, exploiting the "symmetry" of structure and information across KBs, suffer in the presence of asymmetry, which is frequent as KBs are independently built. Specifically, we observe three types of asymmetries (in concepts, in features, and in structures). Our goal is to identify key techniques to reduce accuracy loss caused by each type of asymmetry, then design Asymmetry-Resistant Instance Alignment framework (ARIA). ARIA uses two-phased blocking methods considering concept and feature asymmetries, with a novel similarity measure over-coming structure asymmetry. Compared to a state-of-the-art method, ARIA increased precision by 19% and recall by 2%, and decreased processing time by more than 80% in matching large-scale real-life KBs.

AB - We study the problem of instance alignment between knowledge bases (KBs). Existing approaches, exploiting the "symmetry" of structure and information across KBs, suffer in the presence of asymmetry, which is frequent as KBs are independently built. Specifically, we observe three types of asymmetries (in concepts, in features, and in structures). Our goal is to identify key techniques to reduce accuracy loss caused by each type of asymmetry, then design Asymmetry-Resistant Instance Alignment framework (ARIA). ARIA uses two-phased blocking methods considering concept and feature asymmetries, with a novel similarity measure over-coming structure asymmetry. Compared to a state-of-the-art method, ARIA increased precision by 19% and recall by 2%, and decreased processing time by more than 80% in matching large-scale real-life KBs.

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M3 - Conference contribution

AN - SCOPUS:84908173811

T3 - Proceedings of the National Conference on Artificial Intelligence

SP - 94

EP - 100

BT - Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence

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Lee S, Hwang SW. ARIA: AsymmetRy Resistant Instance Alignment. In Proceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence. AI Access Foundation. 2014. p. 94-100. (Proceedings of the National Conference on Artificial Intelligence).

ARIA: AsymmetRy Resistant Instance Alignment

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Bibliographical note

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