NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
This addresses the challenge for developers and end-users in service-oriented computing by reducing the need for formal semantics and translation mechanisms, though it is incremental over existing natural language-based methods.
The paper tackles the problem of service composition by proposing an unrestricted natural language approach using sentence embeddings, which reduces development effort by more than 44% while maintaining high precision and recall in matching user requests with service invocations.
Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.