Scalable Uncertainty Reasoning in Knowledge Graphs
Quick Take
The thesis proposes a modular framework for scalable uncertainty reasoning in knowledge graphs.
Key Points
- Addresses uncertainty in knowledge graphs at three levels.
- Develops techniques for probabilistic literals and query algebra.
- Introduces topology-aware geometric embeddings for schema reasoning.
📖 Reader Mode
~2 min readAbstract: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 naïve 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.
| Comments: | 14 pages. Preprint of a paper accepted at the ESWC 2026 PhD Symposium |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16568 [cs.AI] |
| (or arXiv:2605.16568v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16568 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jingcheng Wu [view email]
[v1]
Fri, 15 May 2026 19:16:10 UTC (38 KB)
— Originally published at arxiv.org
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