Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition
| Autor: | Xie, Xihao, Zhang, Jia, Ramachandran, Rahul, Lee, Tsengdar J., Lee, Seungwon |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 10 pages, 15 figures, 1 table |
| Databáze: | arXiv |
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