# Fuzzy Quantification over OWL Ontologies and Knowledge Graphs

arXiv: https://arxiv.org/abs/2606.25778 · HTML: https://mlsanity.com/paper/2606.25778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 54 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting](https://mlsanity.com/paper/2008.05297/) (2020)
- [PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL Ontologies](https://mlsanity.com/paper/2303.07192/) (2023)

## Abstract (arXiv)

This paper presents a versatile framework for evaluating fuzzy quantification queries over both standard and fuzzy ontologies as well as knowledge graphs. The primary objective is the retrieval of individuals that satisfy queries articulated via Type I or Type II fuzzy quantified expressions. A key advantage of the proposed approach is its inherent adaptability: it remains entirely agnostic to the quantifier type, the underlying evaluation method, and the specific data source of the ontology (i.e., OWL ontologies or RDFS knowledge graphs). Furthermore, we present Q2S2, a publicly accessible implementation of this system developed to support future research.