FAIRPORTDomain-Specific

Metadata Using W3C DCAT & SKOS

with Ontology Views9 April 2014

Tim Clark Massachusetts General Hospital

Harvard Medical School

© 2014 Massachusetts General Hospital

Fairport Metadata: Use Case 1UC1 - 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>

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.

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.

Fairport Metadata: Use Cases 2 & 3UC2 - 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

Faceted search/browse example

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

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.

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”)

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.

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

Core Dataverse metadata terms

Domain specific metadata terms