Fuzzy Quantification over OWL Ontologies and Knowledge Graphs

2026-06-24

AI Summary

This work addresses the challenge of evaluating fuzzy quantified queries over OWL ontologies and knowledge graphs by proposing a general, decoupled framework for fuzzy quantification assessment. The framework supports diverse fuzzy quantifier types—including Type I and Type II—and is compatible with both standard and fuzzy-semantic data sources. By integrating fuzzy logic with Semantic Web technologies (OWL/RDFS), it enables flexible parsing and efficient evaluation of fuzzy quantified expressions through an extensible query processing mechanism. An open-source system, Q2S2, implemented based on this framework, demonstrates its generality, effectiveness, and practicality through empirical validation on real-world ontologies and knowledge graphs.

Abstract

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.

Problem

• fuzzy quantification
• OWL ontologies
• knowledge graphs
• fuzzy queries
• individual retrieval

Innovation

• fuzzy quantification
• ontology querying
• knowledge graphs
• Q2S2
• agnostic framework