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.

本论文研究了语义 Web 机器学习（SWeML）系统，评估了其特征和趋势，并提出了一个本体分类系统。结果表明，SWeML 系统应用广泛，特别是在深度学习和知识图谱技术的推动下，呈快速增长趋势。