Conformal multi-hop relation detection and classification in knowledge graphs

Frederick Law
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 266:173-192, 2025.

Abstract

Knowledge graphs (KGs) have seen an increasing use in application domains where information may be deemed proprietary, protected, or sensitive, such as enterprise, medical, or security applications. For such systems, incorporating uncertainty quantification (UQ) is critically necessary when KG information is passed to others for any downstream usage. Moreover, such systems often have constraints on data availability due to safety or legal restrictions, and as such full access to well-labeled training data may be unavailable. Conformal prediction is a distribution-free UQ strategy which is well-equipped to handle both of these concerns, as it produces prediction sets with statistically valid guarantees and is highly compatible with black-box models, which may be shared more easily than training data. In this work, we develop a novel conformal framework for simultaneously detecting and classifying multi-hop relations between entities in a KG, which only assumes access to a pre-trained KG model over triples and does not require multi-hop training data. Our framework utilizes a greedy approach, wherein we use successive conformal predictors to build a sparsely-supported scoring function in the high-dimensional multi-hop relation space. In numerical experiments on publicly available benchmark KGs with variable size and multi-hop length, our conformal multi-hop relation sets offer substantial reduction relative to the multi-hop relation space.

Cite this Paper

BibTeX
@InProceedings{pmlr-v266-law25a, title = {Conformal multi-hop relation detection and classification in knowledge graphs}, author = {Law, Frederick}, booktitle = {Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {173--192}, year = {2025}, editor = {Nguyen, Khuong An and Luo, Zhiyuan and Papadopoulos, Harris and Löfström, Tuwe and Carlsson, Lars and Boström, Henrik}, volume = {266}, series = {Proceedings of Machine Learning Research}, month = {10--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v266/main/assets/law25a/law25a.pdf}, url = {https://proceedings.mlr.press/v266/law25a.html}, abstract = {Knowledge graphs (KGs) have seen an increasing use in application domains where information may be deemed proprietary, protected, or sensitive, such as enterprise, medical, or security applications. For such systems, incorporating uncertainty quantification (UQ) is critically necessary when KG information is passed to others for any downstream usage. Moreover, such systems often have constraints on data availability due to safety or legal restrictions, and as such full access to well-labeled training data may be unavailable. Conformal prediction is a distribution-free UQ strategy which is well-equipped to handle both of these concerns, as it produces prediction sets with statistically valid guarantees and is highly compatible with black-box models, which may be shared more easily than training data. In this work, we develop a novel conformal framework for simultaneously detecting and classifying multi-hop relations between entities in a KG, which only assumes access to a pre-trained KG model over triples and does not require multi-hop training data. Our framework utilizes a greedy approach, wherein we use successive conformal predictors to build a sparsely-supported scoring function in the high-dimensional multi-hop relation space. In numerical experiments on publicly available benchmark KGs with variable size and multi-hop length, our conformal multi-hop relation sets offer substantial reduction relative to the multi-hop relation space.} }
Endnote
%0 Conference Paper %T Conformal multi-hop relation detection and classification in knowledge graphs %A Frederick Law %B Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2025 %E Khuong An Nguyen %E Zhiyuan Luo %E Harris Papadopoulos %E Tuwe Löfström %E Lars Carlsson %E Henrik Boström %F pmlr-v266-law25a %I PMLR %P 173--192 %U https://proceedings.mlr.press/v266/law25a.html %V 266 %X Knowledge graphs (KGs) have seen an increasing use in application domains where information may be deemed proprietary, protected, or sensitive, such as enterprise, medical, or security applications. For such systems, incorporating uncertainty quantification (UQ) is critically necessary when KG information is passed to others for any downstream usage. Moreover, such systems often have constraints on data availability due to safety or legal restrictions, and as such full access to well-labeled training data may be unavailable. Conformal prediction is a distribution-free UQ strategy which is well-equipped to handle both of these concerns, as it produces prediction sets with statistically valid guarantees and is highly compatible with black-box models, which may be shared more easily than training data. In this work, we develop a novel conformal framework for simultaneously detecting and classifying multi-hop relations between entities in a KG, which only assumes access to a pre-trained KG model over triples and does not require multi-hop training data. Our framework utilizes a greedy approach, wherein we use successive conformal predictors to build a sparsely-supported scoring function in the high-dimensional multi-hop relation space. In numerical experiments on publicly available benchmark KGs with variable size and multi-hop length, our conformal multi-hop relation sets offer substantial reduction relative to the multi-hop relation space.
APA
Law, F.. (2025). Conformal multi-hop relation detection and classification in knowledge graphs. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:173-192 Available from https://proceedings.mlr.press/v266/law25a.html.

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