Fuzzy Ontology and Description Logics for Information Systems
Summary
Fuzzy ontology and description logics constitute a formal framework for representing and reasoning about knowledge that is inherently vague, imprecise or uncertain. Traditional ontologies employ crisp class–subclass relationships and binary predicates, but many real-world domains require graduated notions of belonging and similarity: for example, in medical diagnosis an anatomical structure may only partially satisfy the criteria for a disease category, or in e-commerce a product may share traits with multiple user preferences to differing extents. Fuzzy ontology extends the standard ontology model by attaching degrees of membership, typically drawn from the unit interval, to concepts and roles. Description logics provide the underlying formal language and reasoning machinery, enabling consistency checking, concept subsumption and instance classification under fuzzy semantics. Together, these approaches support the construction of knowledge bases that accommodate vagueness at the conceptual level while retaining desirable computational properties. Recent efforts have focused on scalable reasoning algorithms, probabilistic extensions, modular ontology design and practical tool support, with applications spanning healthcare, environmental monitoring, multimedia retrieval and intelligent decision support.
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Fuzzy Ontology and Description Logics for Information Systems publication trend
The graph below shows the total number of articles in fuzzy ontology and description logics for information systems across all publications each year (not limited to Nature Index journals).
Technical terms
Fuzzy ontology: A knowledge representation structure in which concepts and relationships are associated with degrees of truth or membership to capture vagueness in domain knowledge.
Description logic: A family of formal languages for defining and reasoning about the concepts, roles and individuals of an ontology, offering decidable inference services such as consistency checking and subsumption.
T-norm: A triangular norm used to generalise logical conjunction in fuzzy set theory, enabling the combination of membership degrees under different semantics.
Fuzzy Bayesian network: A hybrid probabilistic model that integrates fuzzy variables and membership functions with Bayesian inference to represent both vagueness and statistical uncertainty.
Reasoning: The computational process of deriving implicit knowledge from explicitly represented concepts and relationships, such as inferring whether an individual belongs to a fuzzy concept or whether one fuzzy concept subsumes another.
References
- ProbFuzzOnto: A Fuzzy Ontology-Driven Uncertainty Approach Using Fuzzy Bayesian Networks. International Journal of Fuzzy Systems (2024).
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