Search

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). [ABSTRACT FROM AUTHOR]

There is an increasing number of projects based on Knowledge Graphs and SPARQL endpoints. These SPARQL endpoints are later queried by final users or used to feed many different kinds of applications. Shape languages, such as ShEx and SHACL, have emerged to guide the evolution of these graphs and to validate their expected topology. However, authoring shapes for an existing knowledge graph is a time-consuming task. The task gets more challenging when dealing with sources, possibly maintained by heterogeneous agents. In this paper, we present sheXer, a system that extracts shapes by mining the graph structure. We offer sheXer as a free Python library capable of producing both ShEx and SHACL content. Compared to other automatic shape extractors, sheXer includes some novel features such as shape inter-linkage and computation of big real-world datasets. We analyze the features and limitations w.r.t. performance with different experiments using the English chapter of DBpedia. [ABSTRACT FROM AUTHOR]