PROCS: Uncertainty-aware knowledge graph embeddings via conceptual spaces

Azam, Ubaid, Razzak, Imran, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 and Jameel, Shoaib 2026. PROCS: Uncertainty-aware knowledge graph embeddings via conceptual spaces. Presented at: CIKM 2026, Rome, Italy, 07-11 November 2026. Proceedings of the 35th ACM International Conference on Information and Knowledge Management. New York, NY: ACM, pp. 6877 - 6883. 10.1145/3799682.3839959

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Abstract

While deep reasoning models (e.g., NBFNet, ULTRA) have advanced knowledge graph completion, they often sacrifice the efficiency and interpretability of geometric embeddings for increased expressivity. We present PROCS, a framework that combines the best of both worlds through region-based representations inspired by Gärdenfors' Conceptual Spaces, realised as a scalable probabilistic embedding layer. Unlike deterministic point embeddings, PROCS models entities as Gaussian distributions where variance explicitly encodes vagueness and conflict. By introducing a scalable geometric inference mechanism that avoids intractable sampling, PROCS achieves 0.417 MRR on FB15k-237, matching the performance of heavy reasoning models while retaining the O(1) inference of embedding methods. Its uncertainty estimates enable risk-aware retrieval, validated by a Defence-domain case study. Our code and data are available at https://github.com/ubaidazam/PROCS.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	ACM
ISBN:	9798400725395
Date of First Compliant Deposit:	13 November 2026
Last Modified:	13 Nov 2026 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/190901

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