The field of Virtual Knowledge Graphs (VKGs) continues to grow in both academic and applied contexts. Yet, the issue of updates in VKG systems has not yet received adequate attention, although it is crucial to manage data modifications at the data source level through the lens of an ontology. In this paper, we focus on VKGs whose ontology is specified in the lightweight ontology language DL-Lite 𝐴 , and we propose diverse settings and research directions we intend to explore to address the challenge of translating ontology-based updates into updates at the level of data sources. We also pay attention to the important problem of automated analysis of mappings, which plays a major role when it comes to reformulating ontology-based update requests into update requests over the data sources.
Clinical Decision Support (CDS) is designed to provide medical guidance and decision support based on patient information. More and more patient data is being collected from medical devices and fitness trackers, and some decisions need to be made quickly to allow for timely medical treatment. In our work, we combine a Semantic Web rule-based approach for representing medical guidelines with medical data represented in the “Fast Healthcare Interoperability Resources” (FHIR) standard. We implement a complex event processing strategy to deliver near-real-time decisions for a physician. This is relevant in intensive care and emergency medicine in particular, as medical treatment is time-critical and can decide on life or death of the patients. Different approaches exist for real-time clinical decision support. However, the integration of FHIR and Semantic Web technologies is missing. By integrating FHIR, medical ontologies, and Prova [1] as a semantic rule engine, we can define the medical guideline and include hooks for data extraction and decision points. We describe the entire process, starting with the medical device submitting the FHIR observations, through the data handling and decision points, to the notification of the attending physician. We extend the functionality of the existing rule engine for data handling and decision-making. In this work, we outline a solution that is capable of clinical decision support, including FHIR, and makes use of a semantic rule engine that allows medical guidelines to be expressed in RuleML. With these features, we also include the option of real-time complex event processing.
