Towards Semantic Integration For Explainable Artificial Intelligence In The Biomedical Domain

Explainable artificial intelligence typically focuses on data-based explanations, lacking the semantic context needed to produce human-centric explanations. This is especially relevant in healthcare and life sciences where the heterogeneity in both data sources and user expertise, and the underlying complexity of the domain and applications poses serious challenges. The Semantic Web represents an unparalleled opportunity in this area: it provides large amounts of freely available data in the form of Knowledge Graphs, which link data to ontologies, and can thus act as background knowledge for building explanations closer to human conceptualizations. In particular, knowledge graphs support the computation of semantic similarity between objects, providing an understanding of why certain objects are considered similar or different. This is a basic aspect of explainability and is at the core of many machine learning applications. However, when data covers multiple domains, it may be necessary to integrate different ontologies to cover the full semantic landscape of the underlying data. We propose a methodology for semantic explanations in the biomedical domain that is based on the semantic annotation and integration of heterogenous data into a common semantic landscape that supports semantic similarity assessments. This methodology builds upon state of the art semantic web technologies and produces post-hoc explanations that are independent of the machine learning method employed.