Scalable Uncertainty Reasoning in Knowledge Graphs

Computer Science > Artificial Intelligence [Submitted on 15 May 2026] Scalable Uncertainty Reasoning in Knowledge Graphs Jingcheng Wu 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 Focus to learn more Submission history From: Jingcheng Wu [view email] [v1] Fri, 15 May 2026 19:16:10 UTC (38 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)