Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications

Pouya Pezeshkpour
Pouya Pezeshkpour
Yifan Tian
Yifan Tian

North American Chapter of the Association for Computational Linguistics, pp. 3336-3347, 2019.

Cited by: 5|Bibtex|Views16
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for link prediction mode...More

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