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 components with techniques developed by the Semantic Web community—Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast 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 past 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 article is a classification system for SWeML Systems that we publish as ontology.

与人工智能研究创建结合学习和符号组件的智能系统的总体趋势相一致，出现了一个新的子领域，该领域专注于将机器学习组件与语义网社区开发的技术相结合——语义网机器学习（SWeML） ）。由于其在过去二十年中的快速增长和对多个社区的影响，因此需要更好地了解这些 SWeML 系统的空间、其特征和趋势。然而，缺乏采用原则性和公正方法的调查。为了填补这一空白，我们进行了系统研究并分析了过去十年在该领域发表的近 500 篇论文，其中我们重点评估架构和特定于应用程序的功能。我们的分析发现人们对 SWeML 系统的兴趣迅速增长，对多个应用领域和任务有很大影响。这种快速增长的催化剂是深度学习和知识图技术应用的增加。通过利用通过本研究获得的对该领域的深入了解，本文的另一个关键贡献是我们作为本体发布的 SWeML 系统的分类系统。