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.
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Notes
- 1.
The third lifecycle in a SWeMLS, being the Application lifecycle corresponds to the extensively discussed Software Development Lifecycle, which we will not discuss in the scope of this paper.
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Funding and Acknowledgement
This work has been funded by the project OBARIS (https://www.obaris.org/), which has received funding from the Austrian Research Promotion Agency (FFG) under grant 877389.
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Breit, A., Waltersdorfer, L., Ekaputra, F.J., Miksa, T., Sabou, M. (2022). A Lifecycle Framework for Semantic Web Machine Learning Systems. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_33
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