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SIGIR 2024 T3.4 [fp] Untargeted Adversarial Attack on Knowledge Graph Embeddings
Description:
Learn about untargeted adversarial attacks on knowledge graph embeddings in this 11-minute conference presentation from SIGIR 2024. Explore research findings presented by authors Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng and Jun Liu as part of the Privacy, Security and Federated Learning track. Gain insights into security vulnerabilities and attack mechanisms that can affect knowledge graph embedding models, which are crucial components in many modern AI and information retrieval systems.

Untargeted Adversarial Attack on Knowledge Graph Embeddings

Association for Computing Machinery (ACM)
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