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Knowledge base completion meets transfer learning

Abstract:

The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open knowledge bases, i.e., knowledge bases where more than one copy of a real-world entity or relation may exist. Such knowledge bases are a natural ou...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
Association for Computational Linguistics
Host title:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Pages:
6521–6533
Publication date:
2021-11-09
Acceptance date:
2021-08-26
Event title:
2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)
Event location:
Punta Cana, Dominican Republic
Event website:
https://2021.emnlp.org/
Event start date:
2021-11-07
Event end date:
2021-11-11
Language:
English
Keywords:
Pubs id:
1193187
Local pid:
pubs:1193187
Deposit date:
2021-08-30

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