AAAI 2020
Towards Adversarially Robust Knowledge Graph Embeddings
Abstract
Knowledge graph embedding models enable representation learning on multi-relational graphs and are used in security sensitive domains. But, their security analysis has received little attention. I will research security of these models by designing adversarial attacks against them, improving their adversarial robustness and evaluating the effect of proposed improvement on their interpretability.
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Keywords
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Context
- Venue
- AAAI Conference on Artificial Intelligence
- Archive span
- 1980-2026
- Indexed papers
- 28718
- Paper id
- 297539964545535652