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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16568 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 2i9QxgkVcn2S |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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