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

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 - Semanti...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Ekaputra, Fajar J, Llugiqi, Majlinda, Sabou, Marta, Ekelhart, Andreas, Paulheim, Heiko, Breit, Anna, Revenko, Artem, Waltersdorfer, Laura, Farfar, Kheir Eddine, Auer, Sören
Format: Article
Language:English
Subjects:
Artificial intelligence
Deep learning
Knowledge representation
Machine learning
Semantic web
Semantics
Online Access:Get full text
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Summary: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.
ISSN:2331-8422