Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
This work provides a foundational survey for researchers and practitioners in AI and Semantic Web communities, offering an unbiased analysis and organizational framework for SWeML systems.
The paper addresses the need for a systematic understanding of Semantic Web Machine Learning (SWeML) systems by analyzing nearly 500 papers from the last decade, identifying rapid growth driven by deep learning and knowledge graphs, and proposing a classification system published as an ontology.
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.