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Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
Bhardwaj, Peru
;
Kelleher, John
;
Costabello, Luca
;
O'Sullivan, Declan
. - : arXiv, 2021
Abstract:
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. Specifically, to degrade the model's prediction confidence on target facts, we propose to improve the model's prediction confidence on a set of decoy facts. Thus, we craft adversarial additions that can improve the model's prediction confidence on decoy facts through different inference patterns. Our experiments demonstrate that the proposed poisoning attacks outperform state-of-art baselines on four KGE models for two publicly available datasets. We also find that the symmetry pattern based attacks generalize across all model-dataset combinations which indicates the sensitivity of KGE models to this pattern. ... : Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) ...
Keyword:
Artificial Intelligence cs.AI
;
Computation and Language cs.CL
;
FOS Computer and information sciences
;
Machine Learning cs.LG
;
Neural and Evolutionary Computing cs.NE
URL:
https://arxiv.org/abs/2111.06345
https://dx.doi.org/10.48550/arxiv.2111.06345
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