Adversarial Sets for Regularising Neural Link Predictors

Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:61-70, 2017.

Abstract

In adversarial training, a set of models learn together by pursuing competing goals, usu- ally defined on single data instances. How- ever, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the IS-A relation needs to be consistent with the transitivity property: if IS-A(x1, x2) and IS-A(x2, x3) hold, IS-A(x1, x3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is de- fined as a minimax problem, where an adver- sary finds the most offending adversarial ex- amples by maximising the inconsistency loss, and the model is trained by jointly minimis- ing a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neu- ral link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation prob- lem faced by the adversary has efficient closed- form solutions. Experiments on link predic- tion benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-minervini17a, title = {Adversarial Sets for Regularising Neural Link Predictors}, author = {Minervini, Pasquale and Demeester, Thomas and Rockt{\"a}schel, Tim and Riedel, Sebastian}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {61--70}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/minervini17a/minervini17a.pdf}, url = {https://proceedings.mlr.press/r15/minervini17a.html}, abstract = {In adversarial training, a set of models learn together by pursuing competing goals, usu- ally defined on single data instances. How- ever, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the IS-A relation needs to be consistent with the transitivity property: if IS-A(x1, x2) and IS-A(x2, x3) hold, IS-A(x1, x3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is de- fined as a minimax problem, where an adver- sary finds the most offending adversarial ex- amples by maximising the inconsistency loss, and the model is trained by jointly minimis- ing a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neu- ral link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation prob- lem faced by the adversary has efficient closed- form solutions. Experiments on link predic- tion benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Adversarial Sets for Regularising Neural Link Predictors %A Pasquale Minervini %A Thomas Demeester %A Tim Rocktäschel %A Sebastian Riedel %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-minervini17a %I PMLR %P 61--70 %U https://proceedings.mlr.press/r15/minervini17a.html %V R15 %X In adversarial training, a set of models learn together by pursuing competing goals, usu- ally defined on single data instances. How- ever, in relational learning and other non-i.i.d domains, goals can also be defined over sets of instances. For example, a link predictor for the IS-A relation needs to be consistent with the transitivity property: if IS-A(x1, x2) and IS-A(x2, x3) hold, IS-A(x1, x3) needs to hold as well. Here we use such assumptions for deriving an inconsistency loss, measuring the degree to which the model violates the assumptions on an adversarially-generated set of examples. The training objective is de- fined as a minimax problem, where an adver- sary finds the most offending adversarial ex- amples by maximising the inconsistency loss, and the model is trained by jointly minimis- ing a supervised loss and the inconsistency loss on the adversarial examples. This yields the first method that can use function-free Horn clauses (as in Datalog) to regularise any neu- ral link predictor, with complexity independent of the domain size. We show that for several link prediction models, the optimisation prob- lem faced by the adversary has efficient closed- form solutions. Experiments on link predic- tion benchmarks indicate that given suitable prior knowledge, our method can significantly improve neural link predictors on all relevant metrics. %Z Reissued by PMLR on 04 October 2026.
APA
Minervini, P., Demeester, T., Rocktäschel, T. & Riedel, S.. (2017). Adversarial Sets for Regularising Neural Link Predictors. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:61-70 Available from https://proceedings.mlr.press/r15/minervini17a.html. Reissued by PMLR on 04 October 2026.

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