Ontology-Aware Matching Algorithms for Automated API Composition Using Graph-Based Semantic Similarity in Heterogeneous Web Services

Abstract

The proliferation of heterogeneous web services has made the task of automated API composition increasingly complex due to variations in service descriptions, data formats, and domain semantics. Traditional syntactic and keyword-based matching techniques often fail to capture deeper semantic relationships necessary for dynamic service integration. This paper proposes a novel ontology-aware matching framework leveraging graph-based semantic similarity to enhance automated API composition across heterogeneous service environments. By integrating ontological knowledge and graph-based similarity measures, the proposed system can identify semantically related operations even in the presence of heterogeneous naming and structural conventions. Our evaluation across benchmark datasets demonstrates a significant improvement in precision, recall, and composition success rate compared to baseline techniques