Scalable Uncertainty Reasoning in Knowledge Graphs
The paper titled 'Scalable Uncertainty Reasoning in Knowledge Graphs' by Jingcheng Wu addresses the challenges of uncertainty in knowledge graphs. It proposes a modular framework to manage uncertainty at three levels: imprecise attributes, probabilistic triples, and incomplete schema knowledge. The research aims to reconcile semantic precision with computational tractability through specialized reasoning mechanisms.
- ▪Knowledge graphs are essential for semantic data integration but often deal with uncertain real-world data.
- ▪The proposed framework includes techniques for probabilistic literals, SPARQL provenance transformation, and topology-aware geometric embeddings.
- ▪Current Semantic Web standards do not adequately support reasoning over uncertainty, leading to computational challenges.
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Computer Science > Artificial Intelligence arXiv:2605.16568 (cs) [Submitted on 15 May 2026] Title:Scalable Uncertainty Reasoning in Knowledge Graphs Authors:Jingcheng Wu View a PDF of the paper titled Scalable Uncertainty Reasoning in Knowledge Graphs, by Jingcheng Wu View PDF HTML (experimental) Abstract: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.
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