Abstract
Modern decision support systems (DSSs) need components for storing knowledge. Moreover, DSSs must support fuzzy inference to work with uncertainty. Ontologies are designed to represent knowledge of complex structures and to perform inference tasks. Developers must use the OWLAPI and SWRL API libraries to use ontology features. They are impossible to use in DSSs written in programming languages not for Java Virtual Machines. The FuzzyOWL library and the FuzzyDL inference engine are required to work with fuzzy ontologies. The FuzzyOWL library is currently unmaintained and does not have a public Git repository. Thus, it is necessary to develop the ontology service. The ontology service must allow working with ontologies and making fuzzy inferences. The article presents ontology models for decision support, fuzzy inference, and the fuzzy inference algorithm. The article considers examples of DSSs for balancing production capacities and image analysis. The article also describes the architecture of the ontology service. The proposed novel ontology models for decision support make it possible to reduce the time of a knowledge base formation. The ontology service can integrate with external systems with HTTP protocol.
Highlights
Different organizations or individuals need timely decision support [1,2,3,4,5]
The article discusses an approach to decision support systems (DSSs) creation based on an ontology service
The ontology of decision support allows describing the features of a subject area and entities for the decision-making
Summary
Different organizations or individuals need timely decision support [1,2,3,4,5]. Decisionmakers must know the specifics of the context of a subject area for decision support. The context in making decisions determines the conditions and constraints of a subject area [6,7,8]. The context describes the characteristics (numerical and non-numerical values) of the analyzed object and its relationship with other entities of a subject area. The context is easier to formalize in terms of qualitative rather than quantitative values [9]. The large volume and continuous change of data regarding the analyzed object can prevent timely decision-making
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