A Lifecycle Framework for Semantic Web Machine Learning Systems

Abstract

Semantic Web Machine Learning Systems (SWeMLS) characterise applications, which combine symbolic and subsymbolic components in innovative ways. Such hybrid systems are expected to benefit from both domains and reach new performance levels for complex tasks. While existing taxonomies in this field focus on building blocks and patterns for describing the interaction within the final systems, typical lifecycles describing the steps of the entire development process have not yet been introduced. Thus, we present our SWeMLS lifecycle framework, providing a unified view on Semantic Web, Machine Learning, and their interaction in a SWeMLS. We further apply the framework in a case study based on three systems, described in literature. This work should facilitate the understanding, planning, and communication of SWeMLS designs and process views.