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Fuzzy Quantification over OWL Ontologies and Knowledge Graphs: A Versatile Framework
New research presents a versatile framework for evaluating fuzzy quantification queries over standard and fuzzy ontologies as well as knowledge graphs, agnostic to quantifier type and evaluation method.
A paper posted on arXiv on June 25 presents a versatile framework for evaluating fuzzy quantification queries over both standard and fuzzy ontologies as well as knowledge graphs. The primary objective is retrieving individuals that satisfy queries articulated via Type I or Type II fuzzy quantified expressions. A key advantage is its inherent adaptability: the framework remains entirely agnostic to the quantifier type, the underlying evaluation method, and the knowledge representation formalism. The paper is available under arXiv ID 2606.25778 in the cs.AI category, offering a unified solution for fuzzy semantic queries with broad applications in information retrieval, knowledge discovery, and decision support.
Why it matters
This framework provides a highly flexible general solution for fuzzy quantification queries with broad applicability in semantic retrieval and knowledge discovery.