Fuzzy Quantification over OWL Ontologies and Knowledge Graphs
Quick Answer
This paper introduces a flexible framework for evaluating fuzzy quantification queries across OWL ontologies and knowledge graphs, allowing retrieval of individuals based on Type I or Type II fuzzy quantified expressions.
Quick Take
This paper introduces a flexible framework for evaluating fuzzy quantification queries across OWL ontologies and knowledge graphs, allowing retrieval of individuals based on Type I or Type II fuzzy quantified expressions. The approach is adaptable to various quantifier types and data sources, and includes Q2S2, a public implementation for future research support.
Key Points
- Framework supports fuzzy quantification over standard and fuzzy ontologies.
- Retrieves individuals based on Type I or Type II fuzzy quantified expressions.
- Adaptable to various quantifier types and evaluation methods.
- Q2S2 is a publicly accessible implementation for research.
- Enhances flexibility in querying knowledge graphs and ontologies.
Paper Resources
📖 Reader Mode
~1 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.25778 [cs.AI] |
| (or arXiv:2606.25778v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.25778 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fernando Bobillo [view email]
[v1]
Wed, 24 Jun 2026 12:56:05 UTC (468 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →The Verification Horizon: No Silver Bullet for Coding Agent Rewards
As coding agents evolve, verifying solutions becomes more challenging than generating them, necessitating a focus on scalable, faithful, and robust verification methods. The study reveals that no fixed reward function can sustain effectiveness as model capabilities advance, emphasizing the need for verification to evolve alongside solution generation.