Scholia, Scientometrics and Wikidata

Nielsen, Finn Årup; Mietchen, Daniel; Willighagen, Egon

doi:10.1007/978-3-319-70407-4_36

Cited by 83 publications

(63 citation statements)

References 29 publications

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“…Some of these are generalpurpose semantic tools like search engines or personal assistants [1], while others are tailored for specific scientific communities, e.g. Wikigenomes [18] for curating microbial genomes, WikiDP for digital preservation of software [26], or Scholia [13] for exploring scholarly publications. Through such tools, communities that are not active on Wikidata can engage with the Wikidata RDF graph.…”

Section: Domain-generic Shex Validation In Wikidatamentioning

confidence: 99%

Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation

Thornton

Solbrig

Stupp

et al. 2019

Lecture Notes in Computer Science

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We discuss Shape Expressions (ShEx), a concise, formal, modeling and validation language for RDF structures. For instance, a Shape Expression could prescribe that subjects in a given RDF graph that fall into the shape "Paper" are expected to have a section called "Abstract", and any ShEx implementation can confirm whether that is indeed the case for all such subjects within a given graph or subgraph. There are currently five actively maintained ShEx implementations. We discuss how we use the JavaScript, Scala and Python implementations in RDF data validation workflows in distinct, applied contexts. We present examples of how ShEx can be used to model and validate data from two different sources, the domain-specific Fast Healthcare Interoperability Resources (FHIR) and the domain-generic Wikidata knowledge base, which is the linked database built and maintained by the Wikimedia Foundation as a sister project to Wikipedia. Example projects that are using Wikidata as a data curation platform are presented as well, along with ways in which they are using ShEx for modeling and validation. When reusing RDF graphs created by others, it is important to know how the data is represented. Current practices of using human-readable descriptions or ontologies to communicate data structures often lack sufficient precision for data consumers to quickly and easily understand data representation details. We provide concrete examples of how we The original version of this chapter was revised: By mistake the chapter was originally published non open access. The correction to this chapter is available at

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Section: Domain-generic Shex Validation In Wikidatamentioning

confidence: 99%

Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation

Thornton

Solbrig

Stupp

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…The second use case is to demonstrate how we can link virus gene and protein information to literature. Here, we used Scholia ( https://tools.wmflabs.org/scholia/ ) as a central tool (13) . It provides a graphical interface around data in Wikidata, for example literature about a specific coronavirus protein (e.g.…”

Section: Scholiamentioning

confidence: 99%

“…The Gene Wiki project has been tearing down the different research silos on genetics, biological processes, related diseases and associated drugs (10) . In contrast to legacy databases, where data models follow a relational data schema of connected tables, Wikidata ( https://wikidata.org/ ) uses statements to store facts (see Figure 1) (10)(11)(12)(13) . This model of statements aligns well with the RDF triple model of the semantic web and the content of Wikidata is also serialized as Resource Description Framework (RDF) triples (14,15) , acting as stepping stone for data resources to the semantic web.…”

Section: Introductionmentioning

confidence: 99%

A protocol for adding knowledge to Wikidata, a case report

Waagmeester

Willighagen

et al. 2020

Preprint

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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 modelled with entity schemas represented by Shape Expressions. 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 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. 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).

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“…In "Scholia, Scientometrics and Wikidata," Finn Årup Nielsen, Daniel Mietchen, and Egon Willighagen describe their work on Scholia, 9 an open source tool to build scholarly and institutional profiles on the basis of bibliographic and related data in Wikidata. 10 Since Wikidata is a crowdsourced resource like Wikipedia, it suffers from unevenness. Much more information exists about churches, for example, in New York City than in Indianapolis.…”

Section: Introduction To Wikidatamentioning

confidence: 99%