Abstract
Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false negative issue, i.e., the sampled negative training instances may include potential true facts; and 2) the data sparsity issue, i.e., true facts account for only a tiny part of all possible facts. To this end, we propose positive-unlabeled learning with adversarial data augmentation (PUDA) for KGC. In particular, PUDA tailors positive-unlabeled risk estimator for the KGC task to deal with the false negative issue. Furthermore, to address the data sparsity issue, PUDA achieves a data augmentation strategy by unifying adversarial training and positive-unlabeled learning under the positive-unlabeled minimax game. Extensive experimental results on real-world benchmark datasets demonstrate the effectiveness and compatibility of our proposed method.
Original language | English (US) |
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Title of host publication | Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 |
Editors | Luc De Raedt, Luc De Raedt |
Publisher | International Joint Conferences on Artificial Intelligence Organization |
Pages | 2248-2254 |
Number of pages | 7 |
ISBN (Electronic) | 9781956792003 |
DOIs | |
State | Published - 2022 |
Event | 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria Duration: Jul 23 2022 → Jul 29 2022 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 |
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Country/Territory | Austria |
City | Vienna |
Period | 07/23/22 → 07/29/22 |
Bibliographical note
Publisher Copyright:© 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence