Semantics-based API discovery, matching and composition with linked metadata

Web APIs have been adopted as the de facto standard for exchanging data on the Web. However, engineering applications that orchestrate the invocation of multiple APIs and the data flow among them are still mostly manual and labor intensive. In fact, as the number of the potentially relevant APIs increases, compositions become opaque, difficult to maintain, and practically impossible to reuse. The recent advances around linked data formalisms have the potential to provide “usable” semantics, to enable automatic API composition methods. In this paper, we formalize a simplified description model, based on SPARQL graph patterns, for capturing the semantics of Web APIs. Based on this model, we propose a methodology for a fully automated process that produces semantically valid composition chains, using iterative subgraph isomorphism. We have validated the usefulness and accuracy of our approach, using a collection of publicly available Web APIs relevant to a real-world use cases.