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Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
Fairport domain specific metadata using w3 c dcat & skos w ontology views
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Fairport domain specific metadata using w3 c dcat & skos w ontology views

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FAIRPORT is an international project to develop a lightweight interoperability architecture for biomedical - and potentially other - data repositories. …

FAIRPORT is an international project to develop a lightweight interoperability architecture for biomedical - and potentially other - data repositories.
This slide deck is a presentation to the FAIRPORT technical team. It describes a proposed model for supporting domain-specific search metadata using a common schema model across all repositories.
The proposal makes use of the following existing technologies, with minor extensions:
- the W3C DCAT model for dataset description
- the W3C SKOS knowledge organization system
- OWL2 Ontology Language
- Dublin Core Vocabulary
- NCBO Bioportal biomedical ontologies collection

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  • 1. FAIRPORT Domain-Specific Metadata Using W3C DCAT & SKOS with Ontology Views 9 April 2014 Tim Clark Massachusetts General Hospital Harvard Medical School © 2014 Massachusetts General Hospital
  • 2. Fairport Metadata: Use Case 1 UC1 - Dataset discovery Without knowing the dataset’s UID, find it on the web using a Google-like search, or a faceted search . • Example 1.1: Find datasets relevant to these terms: <Mus musculus> <C57/Bl6J> <LT-HSC> <Flk2> <CD34> <Mouse Genome 430 2.0 Array> • Example 1.2: Find datasets by [all / someOf ] these authors: <Rossi, Derrick J> <Bryder, David> <Zahn, Jacob M>
  • 3. UC 1 Requirements • UC 1 only requires us to be able to search across well-known sets of structures defining datasets, and linked to commonly agreed terms and fields. • In the next example, the repository has its own local vocabularies set up for each facet. • These vocabularies are subsets of terms from various relevant ontologies.
  • 4. UC 2 Requirements • UC 2 requires us to be able to let users specify commonly agreed terms and fields that characterize their datasets, that are drawn from NCBO ontologies. • But without requiring them to choose from the too- large comprehensive sets of terms in NCBO Bioportal • There is also the case of repositories like FigShare, that support only folksonomic tagging.
  • 5. Fairport Metadata: Use Cases 2 & 3 UC2 - Core metadata characterization • Example 2.1: User attaches metadata to indicate the name of the study, the authors, the date and version. UC3 - Domain-specific metadata characterization: • Example 3.1: Indicate the organism species & strain, cell type, associated gene names, and technology platform used to produce the dataset
  • 6. Faceted search/browse example
  • 7. Ontology Views • The repository as a whole implements a “view” on the terms from OBI, EFO, NCI & NCBI Taxon, relevant to its users - its “domain” - by implementing Drupal taxonomies containing the terms & URIs. • There is a much more elegant way to define ontology views, using SKOS and OWL2 punning, outlined in S. Jupp et al.“Taking a view on bio-ontologies”, Proceedings of ICBO 2012, Graz, Austria. • Download PDF: http://ceur-ws.org/Vol-897/session4-paper22.pdf
  • 8. Advantages of Ontology Views • They allow useful term sets from multiple ontologies to be combined. • They allow you to restrict the terms only to those needed in your domain or specific repository. • Avoiding user confusion … • …while preserving generality provided by the underlying ontologies.
  • 9. Domain-specific metadata template in SKOS • Create a domain-specific metadata “template” as a SKOS Concept Scheme, which defines the view your repository takes over a set of ontology terms. • The Concept Scheme has a tree structure. • Top node -> facet -> facetTerm • Facet example: StudyDesignType • facetTerm example: <http://purl.obolibrary.org/obo/OBI_0000951> (OBI, “compound treatement design”)
  • 10. W3C DCAT + SKOS Ontology Views • W3C DCAT already provides a standard dataset description. • It already references SKOS. • DCAT assumes the SKOS Concept Scheme will apply at the whole-repository level. • This may not be the case for multi-domain repositories such as Dryad, Dataverse, Figshare.
  • 11. W3C DCAT Model Each dataset also has a DCAT theme described by terms from a SKOS vocabulary or “concept scheme”. Each dataset and distribution has a set of standard DCMI terms
  • 12. Core Dataverse metadata terms Domain specific metadata terms

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