Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
This addresses the problem of service recommendation for researchers and developers in scientific workflow composition, representing an incremental improvement by adapting NLP methods to this domain.
The paper tackles the challenge of recommending suitable services for scientific workflow composition by proposing an NLP-inspired approach that learns latent service representations from workflow provenance, achieving effective and efficient results as demonstrated on a real-world dataset.
As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel NLP-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service representation from workflow provenance. A workflow composition process is formalized as a step-wise, context-aware service generation procedure, which is mapped to next-word prediction in a natural language sentence. Historical service dependencies are extracted from workflow provenance to build and enrich a knowledge graph. Each path in the knowledge graph reflects a scenario in a data analytics experiment, which is analogous to a sentence in a conversation. All paths are thus formalized as composable service sequences and are mined, using various patterns, from the established knowledge graph to construct a corpus. Service embeddings are then learned by applying deep learning model from the NLP field. Extensive experiments on the real-world dataset demonstrate the effectiveness and efficiency of the approach.