• The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, annotations, using a methodology that conforms to the Architecture of the World Wide Web. This ontology is a non-normative OWL formalization of the textual OA specification at http://www.openannotation.org/spec/core/20130208/index.html @en
  • A vocabulary that supports the publication of Open Data by providing the means to capture machine-readable "rights statements", e.g. the licensing information, copyright notices and attribution requirements that are associated with the publication and re-use of a dataset. @en
  • The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define provenance in a precise, technology-agnostic manner. (4) To support a digital representation of provenance for any 'thing', whether produced by computer systems or not. (5) To allow multiple levels of description to coexist. (6) To define a core set of rules that identify the valid inferences that can be made on provenance representation. @en
  • OPMV, the Open Provenance Model Vocabulary, provides terms to enable practitioners of data publishing to publish their data responsibly. @en
  • PAV is a lightweight ontology for tracking Provenance, Authoring and Versioning. PAV specializes the W3C provenance ontology PROV-O in order to describe authorship, curation and digital creation of online resources. @en
  • The provenance part of PML2 ontology. It is a fundamental component of PML2 ontology. @en
  • The Provenance Vocabulary Core Ontology provides the main classes and properties required to describe provenance of data on the Web. @en
  • Extends the Provenance Vocabulary by defining subclasses of the types of provenance elements introduced in the core ontology. @en
  • An OWL vocabulary to include and exploit probabilistic information in SHACL validation reports @en
  • Quality, architecture, and process are considered the keystones of software engineering. ISO defines them in three separate standards. However, their interaction has been poorly studied, so far. The SQuAP model (Software Quality, Architecture, Process) describes twenty-eight main factors that impact on software quality in banking systems, and each factor is described as a relation among some characteristics from the three ISO standards. Hence, SQuAP makes such relations emerge rigorously, although informally. SQaAP-Ont is an OWL ontology that formalises those relations in order to represent and reason via Linked Data about software engineering in a three-dimensional model consisting of quality, architecture, and process characteristics. @en
  • R4R is a light-weight ontology for representing general relationships of resource for publication and reusing. It asserts that a certain reusing context occurred and determined by its two basic relations, namely, isPackagedWith and isCitedBy. The isPackagedWith relation declares the resource is ready to be reused by incorporating License and Provenance information. The Cites relation is an exceptional to isCitedBy which occurs only two related objects cite each other at the same time. Five resource objects including article, data, code, provenance and license are major class concepts to represent in this ontology. The namespace for all R4R terms is http://guava.iis.sinica.edu.tw/r4r/ @en
  • The Databugger ontology describes concepts used in Databugger, a test driven data-debugging framework that can run automatically generated (based on a schema) and manually generated test cases against an endpoint. @en
  • This vocabulary defines terms used in SHACL, the W3C Shapes Constraint Language. @en
  • The ontology is aimed at the support of research groups in the field of Business Modeling and Knowledge Engineering (BMaKE) in their collaborative work for qualitatively analyzing scholarly papers as well as sharing the results of that analyses and judgements. @en
  • A vocabulary specifying concepts and structures needed to represent different data cubes needed for the Smart Readiness Indicator. @en