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

Thornton, Katherine; Solbrig, Harold R.; Stupp, Gregory S.; Gayo, José Emilio Labra; Mietchen, Daniel; Prud’hommeaux, Eric; Waagmeester, Andra

doi:10.1007/978-3-030-21348-0_39

Cited by 33 publications

(30 citation statements)

References 18 publications

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“…The Wikidata EntitySchema extension does allow checking for conformance. There are currently five actively maintained ShEx implementations that allow checking for ShEx conformance at a larger scale [25] (Fig. 4).…”

Section: Shex Validationmentioning

confidence: 99%

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A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

Waagmeester

Willighagen

et al. 2021

BMC Biol

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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).

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Section: Shex Validationmentioning

confidence: 99%

“…The first step in this approach is to develop the data schema. Wikidata has a schema extension called EntitySchema that uses Shape Expressions (ShEx) as the structural schema language to describe and capture schemas of concepts [25,26]. With ShEx, we describe the RDF structure by which Wikidata content is made available.…”

Section: Introductionmentioning

confidence: 99%

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

Waagmeester

Willighagen

et al. 2021

BMC Biol

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“…ShEx was created in 2014 as a human-readable and concise language for RDF validation and description (Prud'hommeaux, Labra Gayo, & Solbrig, 2014). In 2019, ShEx was adopted by Wikidata to define entity schemas (Thornton et al, 2019) and there is already a directory of entity schemas which have been collaboratively defined by the community.…”

Section: Shex and Entity Schemasmentioning

confidence: 99%

Knowledge graphs and wikidata subsetting

Gayo¹,

Ammar²,

Brickley³

et al. 2021

Preprint

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Knowledge graphs have successfully been adopted by academia, governement and industry to represent large scale knowledge bases. Open and collaborative knowledge graphs such as Wikidata capture knowledge from different domains and harmonize them under a common format, making it easier for researchers to access the data while also supporting Open Science.Wikidata keeps getting bigger and better, which subsumes integration use cases. Having a large amount of data such as the one presented in a scopeless Wikidata offers some advantages, e.g., unique access point and common format, but also poses some challenges, e.g., performance.Regular wikidata users are not unfamiliar with running into frequent timeouts of submitted queries. Due to its popularity, limits have been imposed to allow for fair access to many.However this suppreses many interesting and complex queries that require more computational power and resources. Replicating Wikidata on one's own infrastructure can be a solution which also offers a snapshot of the contents of wikidata at some given point in time. There is no need to replicate Wikidata in full, it is possible to work with subsets targeting, for instance, a particular domain. Creating those subsets has emerged as an alternative to reduce the amount and spectrum of data offered by Wikidata. Less data makes more complex queries possible while still keeping the compatibility with the whole Wikidata as the model is kept. In this paper we report the tasks done as part of a Wikidata subsetting project during the Virtual BioHackathon Europe 2020 and SWAT4(HC)LS 2021, which had already started at NBDC/DBCLS BioHackathon 2019 in Japan, SWAT4(HC)LS hackathon 2019, and Virtual COVID-19 BioHackathon 2019. We describe some of approaches we identified to create subsets and some susbsets from the Life Sciences domain as well as other use cases we also discussed.

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“…Wikidata, shape expressions (ShEx) are used as the structural schema language to describe and capture schema's of concepts (22,23) . It shapes the RDF structure by which Wikidata content is made available.…”

Section: Introductionmentioning

confidence: 99%

A protocol for adding knowledge to Wikidata, a case report

Waagmeester

Willighagen

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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).

show abstract

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

Cited by 33 publications

References 18 publications

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses

Knowledge graphs and wikidata subsetting

A protocol for adding knowledge to Wikidata, a case report

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