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As increasingly more software services have been published onto the Internet,it remains a significant challenge to recommend suitable services to facilitatescientific workflow composition. This paper proposes a novel NLP-inspiredapproach to recommending services throughout a workflow development process,based on incrementally learning latent service representation from workflowprovenance. A workflow composition process is formalized as a step-wise,context-aware service generation procedure, which is mapped to next-wordprediction in a natural language sentence. Historical service dependencies areextracted from workflow provenance to build and enrich a knowledge graph. Eachpath in the knowledge graph reflects a scenario in a data analytics experiment,which is analogous to a sentence in a conversation. All paths are thusformalized as composable service sequences and are mined, using variouspatterns, from the established knowledge graph to construct a corpus. Serviceembeddings are then learned by applying deep learning model from the NLP field.Extensive experiments on the real-world dataset demonstrate the effectivenessand efficiency of the approach.

Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition