Bridging Semantic Web and Machine Learning: First Results of a Systematic Mapping Study

Both symbolic and subsymbolic AI research have seen a recent surge driven by innovative approaches, such as neural networks and knowledge graphs. Further opportunities lie in the combined use of these two paradigms in ways that benefit from their complementary strengths. Accordingly, there is much research at the confluence of these two research areas and a number of efforts were already made to survey and analyze the resulting research area. However, to our knowledge, none of these surveys rely on methodologies that aim to capture an evidence-based characterization of the area while at the same time being reproducible. To fill in this gap, in this paper we report on our ongoing work to apply a systematic mapping study methodology to better characterise systems in this area. Given the breadth of the area, we scope the study to focus on systems that combine semantic web technologies and machine learning, which we call SWeML Systems. While the study is still ongoing, we hereby report on its design and the first results obtained.