ARIA: AsymmetRy Resistant Instance Alignment

Sanghoon Lee, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings 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
PublisherAI Access Foundation
Pages94-100
Number of pages7
ISBN (Electronic)9781577356776
Publication statusPublished - 2014 Jan 1
Event28th 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 272014 Jul 31

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Other

Other28th 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
CountryCanada
CityQuebec City
Period14/7/2714/7/31

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.