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.
|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|
|Number of pages||7|
|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
|Name||Proceedings of the National Conference on Artificial Intelligence|
|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|
|Period||14/7/27 → 14/7/31|
Bibliographical notePublisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence