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AAAI 2020

Towards Adversarially Robust Knowledge Graph Embeddings

Short Paper Doctoral Consortium Track Artificial Intelligence

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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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
297539964545535652