Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion

• URL: http://arxiv.org/abs/2205.00904v1
• Date: Mon, 2 May 2022 13:33:27 GMT
• Title: Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
• Authors: Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
• Abstract summary: We propose positive-unlabeled learning with adversarial data augmentation (PUDA) for knowledge graph completion. PUDA tailors positive-unlabeled risk estimator for the KGC task to deal with the false negative issue. To address the data sparsity issue, PUDA achieves a data augmentation strategy by unifying adversarial training and positive-unlabeled learning.
• Score: 41.34363699523586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 candidates for sampling negative training instances 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 demonstrate its effectiveness and compatibility.

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