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Semantic Web Technologies for Explainable Machine Learning Models: A Literature Review

Due to their tremendous potential in predictive tasks, Machine Learning techniques such as Artificial Neural Networks have received great attention from both research and practice. However, often these models do not provide explainable outcomes which is a crucial requirement in many high stakes domains such as health care or transport. Regarding explainability, Semantic Web Technologies offer semantically interpretable tools which allow reasoning on knowledge bases. Hence, the question arises how Semantic Web Technologies and related concepts can facilitate explanations in Machine Learning systems. To address this topic, we present current approaches of combining Machine Learning with Semantic Web Technologies in the context of model explainability based on a systematic literature review. In doing so, we also highlight domains and applications driving the research field and discuss the ways in which explanations are given to the user. Drawing upon these insights, we suggest directions for further research on combining Semantic Web Technologies with Machine Learning.