Artificial Intelligence and Semantic Knowledge Representation for Optimized Web Service Registry Systems

Abstract

The rapid expansion of distributed computing and service-oriented architectures has intensified the need for efficient web service discovery and selection mechanisms. Traditional keyword-based service registries often suffer from ambiguity, low precision, and poor scalability in dynamic environments. This paper presents an integrated approach combining Artificial Intelligence techniques with Semantic Knowledge Representation to optimize web service registry filtering systems. By leveraging ontology-driven frameworks and machine learning-enhanced semantic reasoning, the proposed model improves service matching accuracy, reduces search complexity, and enhances adaptability in heterogeneous service environments. The study builds upon foundational work in semantic-based service filtering mechanisms and extends it by incorporating intelligent decision-making capabilities. Experimental analysis demonstrates that the hybrid approach significantly improves service retrieval performance compared to conventional registry systems. The findings highlight the potential of AI-enabled semantic frameworks in advancing intelligent service-oriented computing.