Fuzzy Quantification over OWL Ontologies and Knowledge Graphs
For researchers working with fuzzy ontologies and knowledge graphs, this work provides a flexible querying framework, though it is an incremental extension of existing fuzzy querying techniques.
This paper introduces a framework for evaluating fuzzy quantification queries over OWL ontologies and knowledge graphs, enabling retrieval of individuals based on Type I or Type II fuzzy quantified expressions. The approach is quantifier-agnostic and data-source-agnostic, with a public implementation (Q2S2) provided.
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.