Semantic Web Machine Learning Systems: An Analysis of System Patterns

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic techniques (a.k.a. neuro-symbolic systems), a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the SemanticWeb (SW) community - SemanticWeb Machine Learning (SWeML for short). Due to the rapid growth of this area and its 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. Of particular interest are the emerging variations of processing patterns used in these systems in terms of their inputs/outputs and the order of the processing units. While several such neuro-symbolic system patterns were identified previously from a large number of papers, there is currently no insight into their adoption in the field, e.g., about the completeness of the introduced system patterns, or about their usage frequency. To fill that gap, we performed a systematic study and analyzed nearly 500 papers published in the last decade in this area, where we focused on evaluating the type and frequency of such system patterns. Overall we discovered 41 different system patterns, which we categorized into six pattern types. In this chapter we detail these pattern types, exemplify their use in concrete papers and discuss their characteristics in terms of their semantic and machine learning modules.