ProbSPARQL extends SPARQL for uncertain numeric data in knowledge graphs

Researchers have developed ProbSPARQL, an extension to the SPARQL query language designed to handle uncertain and multi-dimensional numeric data within knowledge graphs. This new system addresses limitations in current RDF and SPARQL technologies by modeling uncertain numeric values as random variables and enabling distribution-aware queries. Implemented on Apache Jena ARQ and compatible with Fuseki, ProbSPARQL has been tested on real-world data fragments and evaluated for scalability, demonstrating feasible execution and potential speedups over traditional post-processing methods. AI

IMPACT Enhances querying capabilities for knowledge graphs with uncertain numeric data, potentially improving downstream AI applications in areas like product analysis and planning.

RANK_REASON The item is a research paper detailing a new query language extension for knowledge graphs. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• Apache Jena ARQ
• CatalyzeX
• DagsHub
• fuseki
• Gaussian mixture model
• Gotit.pub
• Hugging Face
• ProbSPARQL
• Resource Description Framework
• ScienceCast
• SFB 1574 Circular Factory
• SPARQL

• paper
• infra

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