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

TL;DR

This paper introduces a context-aware, NLP-inspired method for recommending software services during workflow composition by learning latent service representations from workflow provenance and knowledge graphs.

Contribution

It proposes a novel approach that models service recommendation as a next-word prediction task, leveraging deep learning on knowledge graph-derived service sequences.

Findings

Effective service recommendation demonstrated on real-world data

Improved recommendation accuracy over baseline methods

Efficient learning process suitable for large-scale workflows

Abstract

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.