# Scalable Uncertainty Reasoning in Knowledge Graphs

arXiv: https://arxiv.org/abs/2605.16568 · HTML: https://mlsanity.com/paper/2605.16568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 10 of 10)

- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Faithiful Embeddings for EL++ Knowledge Bases](https://mlsanity.com/paper/2201.09919/) (2022)
- [Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models](https://mlsanity.com/paper/2302.06229/) (2023)
- [Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2307.10219/) (2023)
- [Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees](https://mlsanity.com/paper/2510.24754/) (2025)
- [Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review](https://mlsanity.com/paper/2012.09049/) (2020)
- [Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings](https://mlsanity.com/paper/2407.11821/) (2024)
- [Towards Statistical Reasoning in Description Logics over Finite Domains (Full Version)](https://mlsanity.com/paper/1706.03207/) (2017)
- [Statistical EL is ExpTime-complete](https://mlsanity.com/paper/1911.00696/) (2019)

## Built on by (most cited first, 1 of 1)

- [ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data](https://mlsanity.com/paper/2607.18262/) (2026)

## Abstract (arXiv)

Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncertainty manifests in three distinct levels: imprecise attribute values, probabilistic triple existence, and incomplete schema knowledge. However, current Semantic Web standards lack native support for reasoning over such uncertainty, and na"ive extensions often incur computational intractability. In this thesis, I aim to develop a modular framework that addresses each level through tailored techniques: (1) defining probabilistic literals and a corresponding query algebra for continuous attributes; (2) a compilation-based framework transforming SPARQL provenance into tractable probabilistic circuits for uncertain triples; and (3) topology-aware geometric embeddings for statistical schema reasoning. The central hypothesis is that specialized reasoning mechanisms, namely algebraic, logical, and geometric approaches, can reconcile semantic precision with computational tractability.