SUMMARY: Reliable and integrated data are prerequisites for effective research on the recent coronavirus disease 2019 (COVID-19) pandemic. The CovidGraph project integrates and connects heterogeneous COVID-19 data in a knowledge graph, referred to as 'CovidGraph'. It provides easy access to multiple data sources through a single point of entry and enables flexible data exploration. AVAILABILITY AND IMPLEMENTATION: More information on CovidGraph is available from the project website: https://healthecco.org/covidgraph/. Source code and documentation are provided on GitHub: https://github.com/covidgraph. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

An emerging trend in Web computing aims at collecting and integrating distributed data. For instance, various communities recently have build large repositories of structured and interlinked data sets from different Web sources. However, up to date there is virtually no support in navigating, visualising or even analysing structured date sets of this size appropriately. This paper describes novel rendering techniques enabling a new level of visual analytics combined with interactive exploration principles. The underlying visualisation rationale is driven by the principle of providing detail information with respect to qualitative as well as quantitative aspects on user demand while offering an overview at any time. By means of our prototypical implementation and two real-world data sets we show how to answer several data specific tasks by interactive visual exploration.

OWL-S is a combined effort of the Semantic Web and the Web Service community to facilitate an intelligent service provisioning on the Semantic Web. The vision of OWL-S includes automatic service discovery, invocation, composition, orchestration and monitoring of Web-Services through their semantic descriptions. In this paper, we investigate the practical applicability of the current OWL-S specification and show that, in spite of the large momentum of OWL-S, significantly more work needs to be done before the vision of truly intelligent Semantic Web Services can become true. We therefore study the case of an autonomous travel agent that helps users with online hotel arrangements. The aim of our work is twofold: on the one side, we show step-by-step how a prototypical implementation can be realized based on current semantic technologies around UDDI, WSDL, and SOAP. On the other hand, we reveal pitfalls in the current version of OWL-S that severely limit its support for mechanizing service discovery, configuration, combination and automated execution. Throughout the paper, we present practical solutions and workarounds to existing OWL-S shortcomings and hope to therewith further stimulate the ongoing work on Semantic Web Services.