Linked Data is often generated based on a set of declarative rules using languages such as R2RML and RML. These languages are built with machine-processability in mind. It is thus not always straightforward for users to define or understand rules written in these languages, preventing them from applying the desired annotations to the data sources. In the past, graphical tools were proposed. However, next to users who prefer a graphical approach, there are users who desire to understand and define rules via a text-based approach. For the latter, we introduce an enhancement to their workflow. Instead of requiring users to manually write machine-processable rules, we propose writing human-friendly rules, and generate machine-processable rules based on those human-friendly rules. At the basis is YARRRML: a human-readable text-based representation for declarative generation rules. We propose a novel browser-based integrated development environment (IDE) called Matey, showcasing the enhanced workflow. In this work, we describe our demo. Users can experience first hand how to generate triples from data in different formats by using YARRRML's representation of the rules. The actual machine-processable rules remain completely hidden when editing. Matey shows that writing human-friendly rules enhances the workflow for a broader range of users. As a result, more desired annotations will be added to the data sources which leads to more desired Linked Data.

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At the end of 2019, Chinese authorities alerted the World Health Organization (WHO) of the outbreak of a new strain of the coronavirus, called SARS-CoV-2, which struck humanity by an unprecedented disaster a few months later. In response to this pandemic, a publicly available dataset was released on Kaggle which contained information of over 63,000 papers. In order to facilitate the analysis of this large mass of literature, we have created a knowledge graph based on this dataset. Within this knowledge graph, all information of the original dataset is linked together, which makes it easier to search for relevant information. The knowledge graph is also enriched with additional links to appropriate, already existing external resources. In this paper, we elaborate on the different steps performed to construct such a knowledge graph from structured documents. Moreover, we discuss, on a conceptual level, several possible applications and analyses that can be built on top of this knowledge graph. As such, we aim to provide a resource that allows people to more easily build applications that give more insights into the COVID-19 pandemic.