Publications
6PROV-O: The PROV Ontology
Cite
Timothy Lebo, Satya Sahoo, Deborah McGuinness, et al. 2013. PROV-O: The PROV Ontology. In Proceedings of the 22nd International Conference on World Wide Web.
@inproceedings{lebo2013provo, title={PROV-O: The PROV Ontology}, author={Lebo, Timothy and Sahoo, Satya and McGuinness, Deborah and others}, booktitle={Proceedings of the 22nd International Conference on World Wide Web}, year={2013}, doi={10.1145/2487788.2487790} }
PubFlow: provenance-aware workflows for research data publication
In this paper we present a workflow oriented data publication framework called PubFlow. PubFlow is an ongoing research project with the goal to create a framework, which alleviates the process of data publication. A main feature of PubFlow is its provenance
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capturing mechanism. We also present an approach for collecting provenance information in a scientific workflow environment like PubFlow and give an outlook on a data archive for storing provenance information. This archive will be based on a NoSQL graph database and the W3C provenance ontology PROVO.
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Peer Christoph Brauer; Wilhelm Hasselbring; PubFlow: provenance-aware workflows for research data publication; 2013
Performance Evaluation of Upper-Level Ontologies in Developing Materials Science Ontologies and Knowledge Graphs
This study evaluates different upper-level ontologies (BFO, EMMO, PROVO) for creating the Brinell Test Ontology (BTO) and knowledge graphs. It uses Brinell hardness testing as a primary use case to assess query efficiency and semantic richness.
Using a suite of ontologies for preserving workflow-centric research objects
Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or
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modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions. In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects—aggregations of data and metadata that enrich the workflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntington’s disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.
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Khalid Belhajjame; Jun Zhao; Daniel Garijo; Matthew Gamble; Kristina Hettne; Raúl Palma; Eleni Mina; Óscar Corcho; José Manuel Gómez-Pérez; Sean Bechhofer; Graham Klyne; Carole Goble; Using a suite of ontologies for preserving workflow-centric research objects; Journal of Web Semantics; 2015; doi:10.1016/j.websem.2015.01.003
PROV-O: The PROV Ontology Tutorial
Provenance is key for describing the evolution of a resource, the entity responsible for its changes and how these changes affect its final state. A proper description of the provenance of a resource shows who has its attribution and can help resolving whether
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it can be trusted or not. This tutorial will provide an overview of the W3C PROV data model and its serialization as an OWL ontology. The tutorial will incrementally explain the features of the PROV data model, from the core starting terms to the most complex concepts. Finally, the tutorial will show the relation between PROV-O and the Dublin Core Metadata terms.
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Daniel Garijo; PROV-O: The PROV Ontology Tutorial; International Conference on Dublin Core and Metadata Applications; 2013
PROV-O: The PROV Ontology
The PROV Ontology (PROV-O) expresses the PROV Data Model using the OWL2 Web Ontology Language. It provides a set of classes, properties, and restrictions that can be used to represent and interchange provenance information generated in different systems and
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under different contexts. It can also be specialized to create new classes and properties to model provenance information for different applications and domains.
Cite
Timothy Lebo; Satya S. Sahoo; Deborah L. McGuinness; Khalid Belhajjame; James Cheney; David Corsar; Daniel Garijo; Stian Soiland‐Reyes; Stephan Zednik; Jun Zhao; PROV-O: The PROV Ontology; Research Explorer (The University of Manchester); 2013
@misc{w3c2013provo, title={PROV-O: The PROV Ontology}, author={Lebo, Timothy and Sahoo, Satya and McGuinness, Deborah}, year={2013}, publisher={World Wide Web Consortium (W3C)}, howpublished={\url{https://www.w3.org/TR/prov-o/}} }