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

EasyChair Preprint 759

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.

Keyphrases: Abstract Service, Concrete service, Dynamic Service Composition, Middleware, Named Entity Recognition, Natural Language Processing, OSGi bundle, Semantic Web Service, Sentence Embedding, Sentence embeddings, Service Matching, Web Service Composition, composite service, concrete service candidate, effort estimation, natural language based service, pre-trained model, semantic matching, semantic representation, service composition, service composition middleware, service description, service discovery, service execution, unrestricted natural language description

@booklet{EasyChair:759,
  author    = {Oscar Javier Romero López and Ankit Dangi and Sushma Anand Akoju},
  title     = {NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings},
  doi       = {10.29007/3bkb},
  howpublished = {EasyChair Preprint 759},
  year      = {EasyChair, 2019}}