Background: Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons.” Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases. However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modeled with entity schemas represented by Shape Expressions. Results: As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable is demonstrated by integrating data from NCBI (National Center for Biotechnology Information) Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Conclusions: Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, human coronavirus NL63, human coronavirus 229E, human coronavirus HKU1, human coronavirus OC4).

This paper presents the visualization of national budget, a tool based on Semantic Web technologies that shows by graphic representations the Chilean budget law published annually, and their execution by each state agency. We describe the processes for consuming open data from the Budget National Agency, and how this data is transformed and published to linked open data, based on a National Budget Ontology. Although similar initiatives have been developed on transparency and public budget around the world, we consider that there is no previous experience showing optimized access mechanisms both for human and machine readable, providing in each case the highest level of aggregation, granularity and interoperability, making it understandable and easy to process complex data and legislation. As part of our analysis, we describe a recent scenario of usage in the context of the socio-political crisis in Chile, where we discuss the possible impact of the linked open dataset and data visualizations for distribution and control of funds, on the premise that this type of tools can support the decision making and sustainability of public finances. Finally, we present the results of our budget knowledge graph and the lessons learned during the development, allowing to replicate the process and enabling potential uses of the published data in other contexts.

In this paper, we propose an architecture that combines Big Data and Stream Processing which can be applied to the Real Estate Domain. Our approach consists of a specialisation of Lambda architecture and it is inspired by some aspects of Kappa architecture. As a proof of this solution, we show a prototype developed following it and a comparison of the three architecture quality models. Finally, we highlight the differences between the proposed architecture and similar ones and draw some future lines following the present approach.

Several problems arise due to the differences between dentistry and general medicine. The storage of dental data in information silos, the incompatibility of data between different dental clinics or institutions from other medical areas are the most significant ones. The authors propose a decentralized architecture that combines FHIR archetypes, shape expressions, and personal online datastores (PODs) to tackle those issues as follows: FHIR archetypes are used to express the data, shape expressions are used to handle data structure and data access requests, and PODs are used to store information in a decentralized and safe manner that let the owner of the information stored to handle data access. The system allows the patient to store dental information from heterogeneous data sources transparently and respecting the patient's right to autonomy and consent. In this paper, the authors develop this architecture proposal and discuss its relevance and feasibility in the area of dental health.