Interactive NLU-Powered Ontology-Based Workflow Synthesis for FAIR Support of HPC

Abstract

Workflow synthesis is important for automatically creating the data processing workflow in a FAIR data management system for HPC. Previous methods are table-based, rigid and not scalable. This paper addresses these limitations by developing a new approach to workflow synthesis, interactive NLU-powered ontology-based workflow synthesis (INPOWS). IN-POWS allows the use of Natural Language for queries, maximizes the robustness in handling concepts and language ambiguities through an interactive ontology-based design, and achieves superior extensibility by adopting a synthesis algorithm powered by Natural Language Understanding. In our experiments, INPOWS shows the efficacy in enabling flexible, robust, and extensible workflow synthesis.