# NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

## TL;DR

This paper proposes a natural language-based approach for service composition using sentence embeddings, enabling intuitive user requests and reducing development effort without relying on formal semantic descriptions.

## Contribution

It introduces a novel method leveraging sentence embeddings for unrestricted natural language service composition, bypassing traditional semantic formalization.

## Key findings

- Development effort reduced by over 44%
- High precision and recall in matching user requests to services
- Effective semantic representation using sentence embeddings

## Abstract

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.