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.

翻译：顺应人工智能研究中构建结合学习与符号组件的智能系统的总体趋势，一个新兴子领域应运而生，其重点是将机器学习组件与语义网社区开发的技术相结合——即语义网机器学习（简称SWeML）。近二十年来，该领域快速增长并对多个社区产生了深远影响，因此需要更深入地理解这些SWeML系统的空间、其特征及发展趋势。然而，目前尚缺乏采用原则性且无偏见方法的综述研究。为填补这一空白，我们开展了一项系统性研究，分析了过去十年间该领域发表的近500篇论文，重点评估其架构和应用特定特征。分析表明，SWeML系统的关注度迅速攀升，对多个应用领域和任务产生了重要影响。深度学习与知识图谱技术的广泛应用成为这一快速增长的关键催化剂。基于本研究对该领域的深入理解，本文的另一核心贡献是提出了一个针对SWeML系统的分类体系，并将其以本体形式发布。