Towards a Robust Knowledge Graph-Enabled Machine Learning Service Description Framework

Although machine learning (ML) is widely expected to become a key enabler of innovative applications in a number of important domains, building, deploying and managing robust ML pipelines for diverse domains are very challenging as they require expertise in both the application domain as well as the ML field. Recently, machine learning as a service (MLAAS) is being explored as a paradigm to address these challenges and to democratize artificial intelligence (AI). MLAAS envisions an ecosystem with powerful mechanisms for publishing, searching/discovering, composing and deploying ML models. This paper argues that a semantic-rich and flexible ML service description is indispensable for realizing such an ecosystem. Towards this end, we outline a unique approach that leverages knowledge graphs (KGs) for ML service description. A requirements study highlights the ML services aspects that need to be provisioned in the description framework. This paper presents a novel five-dimensional KG-enabled ML service description framework, which incorporates ML task description, Input-Output (I-O) description, ML-model description, dataset and training description and performance characteristics description. In designing this ML service description framework, we introduce several conceptual structures such as functional specifications with semantically-extended types and compound knowledge graphs for representing ML model architectures.