Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG

Fajar J. Ekaputra; Majlinda Llugiqi; Marta Sabou; Andreas Ekelhart; Heiko Paulheim; Anna Breit; Artem Revenko; Laura Waltersdorfer; Kheir Eddine Farfar; Sören Auer

arXiv · 2303.15113

Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG

Fajar J. Ekaputra·Majlinda Llugiqi·Marta Sabou·Andreas Ekelhart·Heiko Paulheim·Anna Breit·Artem Revenko·Laura Waltersdorfer·Kheir Eddine Farfar·Sören Auer

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Abstract

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short). Due to its rapid growth and impact on several communities in the last two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the last decade in this area, where we focused on evaluating architectural, and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this paper is a classification system for SWeML Systems which we publish as ontology.

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Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, Sören Auer. 2023-03-27. Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG. https://arxiv.org/abs/2303.15113

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