Abstract 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 use ShEx as a constraint and validation language that allows humans and machines to communicate unambiguously about data assets. We use ShEx to exchange and understand data models of different origins, and to express a shared model of a resource’s footprint in a Linked Data source. We also use ShEx to agilely develop data models, test them against sample data, and revise or refine them. The expressivity of ShEx allows us to catch disagreement, inconsistencies, or errors efficiently, both at the time of input, and through batch inspections. ShEx addresses the need of the Semantic Web community to ensure data quality for RDF graphs. It is currently being used in the development of FHIR/RDF. The language is sufficiently expressive to capture constraints in FHIR, and the intuitive syntax helps people to quickly grasp the range of conformant documents. The publication workflow for FHIR tests all of these examples against the ShEx schemas, catching non-conformant data before they reach the public. ShEx is also currently used in Wikidata projects such as Gene Wiki and WikiCite to develop quality-control pipelines to maintain data integrity and incorporate or harmonize differences in data across different parts of the pipelines.

extended-abstract Share on Collaboratively Designed Information Structures in Wikipedia Author: Katherine Thornton University of Washington, Seattle, WA, USA University of Washington, Seattle, WA, USAView Profile Authors Info & Claims GROUP '14: Proceedings of the 2014 ACM International Conference on Supporting Group WorkNovember 2014 Pages 275–277https://doi.org/10.1145/2660398.2660440Published:09 November 2014Publication History 0citation74DownloadsMetricsTotal Citations0Total Downloads74Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Category systems have traditionally been created by small committees of people who had authority over the system they were designing. With the rise of large-scale social media systems, category schemes are being created by groups with differing perspectives, values, and expectations for how categories will be used. Prior studies of social tagging and folksonomy focused on the application and evolution of the collective category scheme, but struggled to uncover some of the collective rationale undergirding the decision-making processes in those schemes. In this paper, we qualitatively analyze the early discussions among editors of Wikipedia about the design and creation of its category system. We highlight three themes that dominated the discussion: hierarchy, scope and navigation, and relate these themes to their more formal roots in the information science literature. We distill out four styles of collaboration with regard to category systems that apply broadly to social tagging and other folksonomies. We conclude the paper with implications for collaborative tools and category systems as applied to large-scale collaborative systems.