Fuzzy Quantification over OWL Ontologies and Knowledge Graphs

Researchers have developed a framework for evaluating fuzzy quantification queries over OWL ontologies and knowledge graphs, enabling retrieval of individuals matching Type I or Type II fuzzy quantified expressions. The system is agnostic to quantifier types and data sources, with Q2S2 released as an open implementation for future research.

This paper addresses a significant challenge in semantic web technologies and knowledge representation: the ability to process imprecise or fuzzy queries against structured data sources. Traditional ontology query systems operate on binary logic, returning exact matches or no matches. The framework presented here extends this capability by supporting fuzzy quantification, allowing systems to handle ambiguous or approximate queries that reflect how humans naturally formulate information requests.

The work builds on decades of research in fuzzy logic and semantic web standards. Fuzzy quantifiers (terms like "most," "many," or "few") are common in natural language but difficult to formalize in rigid computational systems. By creating a quantifier-agnostic framework, the researchers enable flexible integration with existing OWL ontologies and RDFS knowledge graphs without requiring system redesign. This flexibility is particularly valuable as organizations increasingly adopt knowledge graphs for enterprise data management.

The practical implications extend to improved information retrieval systems, semantic search engines, and AI applications that must interpret human queries against structured knowledge bases. The release of Q2S2 as a public implementation democratizes access to this technology, potentially accelerating adoption in academic and commercial settings. This is especially relevant for natural language processing systems and intelligent assistants that need to bridge the gap between fuzzy human expressions and precise database queries.

Developers and researchers working with knowledge graphs should monitor implementations of this framework. Future versions may integrate with mainstream semantic web tools, making fuzzy query capabilities standard rather than specialized functionality.

• →A new framework enables fuzzy quantification queries over OWL ontologies and knowledge graphs for more flexible information retrieval.
• →The system remains agnostic to quantifier types and data sources, providing adaptability across different ontology implementations.
• →Q2S2, a public implementation, is now available to support research and practical applications of fuzzy quantification.
• →Fuzzy query capabilities bridge the gap between natural language expressions and rigid database query systems.
• →This work has implications for knowledge graph search, semantic web technologies, and AI-driven information retrieval systems.