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ProbSPARQL extends SPARQL for uncertain numeric data in knowledge graphs

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ProbSPARQL extends SPARQL for uncertain numeric data in knowledge graphs

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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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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jingcheng Wu, Ratan Bahadur Thapa, Daniel Hernandez, Hongkuan Zhou, Steffen Staab ·

    ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data

    arXiv:2607.18262v1 Announce Type: new Abstract: The SFB 1574 Circular Factory is building a shared knowledge graph infrastructure for integrating data about returned products. A central challenge is that circular-factory data include numeric measurements that (i) originate from s…

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