Development of a semantic structure for the composition of cognitive web services - Kasianchuk, Ihor; Petrenko, Anatoliy (Technology audit and production reserves, 2025)
The object of the research is the semantic structure for the composition of cognitive web services. The framework is designed to model, search, and orchestrate cognitive web services, including functionalities such as text recognition, language translation, and sentiment analysis, within dynamic environments. The problem addressed is the lack of efficient and scalable mechanisms for the automated discovery and composition of cognitive web services that can adapt to changing requirements and meet Quality of Service (QoS) constraints. Existing approaches often rely on static rules or keyword-based searches, which fail to provide adequate precision, adaptability, or scalability for complex service ecosystems. The key result of the study is the development of a semantic framework that integrates ontology-based service modeling with logical inference using SWRL (Semantic Web Rule Language) rules. The framework supports dynamic service composition by leveraging semantic relationships between services, input/output data, and constraints such as execution time and accuracy. The results demonstrate higher semantic precision, better adaptability to changes, and improved QoS compliance compared to existing approaches. This is achieved through the use of a formalized ontology for precise service representation, SWRL rules for automated inference, and dynamic service composition based on semantic relationships, which improves query matching and reduces execution time. The proposed framework can be practically applied in environments requiring adaptive service orchestration and composition, such as intelligent automation systems, cloud-based service ecosystems, and IoT (Internet of Things) applications. Its effectiveness is especially evident in scenarios involving complex multiservice workflows where traditional approaches are inefficient. The framework’s extensibility ensures its applicability across various domains, with minimal customization required to incorporate new services or workflows.