Table of Contents

• Getting Started
• A Quick Introduction to RDF
• Sample SPARQL Queries
• RDF Mapping in Opportunities

Getting Started

In order to access the API, you will need an OAuth Consumer Key. Please follow these steps to obtain your Consumer Key:

1. Login with your NETID 
2. Open the "OAuth Consumers" Tab
3. Click on "Add consumer"
4. Under "Consumer name", enter any name for your application/website (ie. MyWebsite.com)
5. Under "Callback URL", enter 'https://YOUR_DOMAIN'.
6. Click 'Save'
7. Click "OAuth Consumers"
8. Click 'Edit' in the row of your newly created 'Consumer'
9. Under 'key' you will find your Consumer Key. Copy this key to a secure location.
1. This key will be used on all the HTTP Requests that you make to the API. 
2. At any given time, you can revoke a consumer (key) and generate a new one.
10. Click 'Save'.
11. Visit the endpoint at http://globalresearchopportunities.northwestern.edu/sparql-api or click on some example queries below.

A Quick Introduction to RDF

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This documents aims to map out the data relationships for Northwestern GRO (Opportunities) though ontologies and RDF mapping. If you are completely unfamiliar with what an ontology is, or would like a better sense of how they work, please refer to additional reading at http://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html.

The benefit of mapping data out as an ontology as opposed to, for example, the relational data exactly as it is stored in the database is interoperability - while we define our own Opportunity, we also equal it to an Opportunity someone else has made. Thus, if you were to have two different data sets, made by two different organizations that are not aware of each other's existence, a good ontology mapping would allow you to use and visualize such data together with ease.

Opportunities uses several existing ontologies - including (click on each for more detail) vivo, skos, and foaf, - and defines it's own ontology, gro. A visual mapping of gro in relation to other ontologies is partially demonstrated by this chart:

Every node in the graph is a Class. A Class can have both data properties and sub-Classes. An example of how the chart could be read in plain language: An Opportunity is an Organization which has TaxonomyTerms such as Credit, Participant, and FundingAvailability, and it also has a 'Geographic Location', which means it has either a 'Geographic Region' or Campus. Equivalent Classes, sub-Classes, and data properties of a Class are referred to as predicates.

These ontologies, as mapped to the Opportunities website, can be exposed through a SPARQL endpoint, such as the one provided through Opportunities at http://globalresearchopportunities.northwestern.edu/sparql-api. This endpoint can return data in various formats including JSON and XML.

For example, we can run the following query to return all predicates, including all sub-Classes and data properties of an Opportunity:

PREFIX gro: <https://globalresearchopportunities.northwestern.edu/ontology/gro.owl#>

 SELECT distinct ?predicate WHERE
 { ?x rdf:type gro:Opportunity .
 ?x ?predicate ?object } 

Sample SPARQL Queries

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Here is a selection of sample SPARQL queries that can be modified and extended to draw various types of data.

PREFIX gro: <https://globalresearchopportunities.northwestern.edu/ontology/gro.owl#>

 SELECT distinct ?x ?predicate ?object WHERE
 { ?x rdf:type gro:Opportunity .
 ?x ?predicate ?object }

This will return every field of every Opportunity, up to however much the API supports. Note that the "gro" prefix was defined prior to the query. Each prefix must be defined with the proper IRI or you may not get any results.

PREFIX foaf: <http://xmlns.com/foaf/0.1/>

 SELECT ?x ?name WHERE
 { ?x rdf:type foaf:Person .
 ?x foaf:name ?name }

This query will return the names of all Users (who are of type foaf:Person). In this example, the ?predicate was replaced with foaf:name to indicate we only want the data field foaf:name, not all predicates.

PREFIX vivo: <http://vivoweb.org/ontology/core#>

SELECT ?x ?object WHERE
{?x rdf:type vivo:School .
 ?x rdfs:label ?object }
 

This will return all Schools (Taxonomy term) you can find Opportunities in. In this example, we used an external ontology not defined by GRO.

PREFIX gro: <https://globalresearchopportunities.northwestern.edu/ontology/gro.owl#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

SELECT distinct ?subject ?object WHERE
{ ?subject rdf:type gro:Language .
 ?subject rdfs:label ?object }
 

This will return all languages you can find Opportunities in. In this example, we used the GRO ontology.

PREFIX gro: <https://globalresearchopportunities.northwestern.edu/ontology/gro.owl#>
PREFIX content: <http://purl.org/rss/1.0/modules/content/>
PREFIX dc: <http://purl.org/dc/terms/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>

SELECT distinct ?subject ?object ?title ?lan WHERE
{ ?subject rdf:type gro:Opportunity .
 ?subject gro:Language ?lan .
 ?subject content:encoded ?object .
 ?subject dc:title ?title }

This query will return all Opportunities and certain fields, such as the language (reference to a Taxonomy Term), their title, and their description.

Or, you can return all Opportunities in the UK - countries are determined by the ISO 3166-1 alpha-2 code of that country (for a full list, http://en.wikipedia.org/wiki/ISO_3166-1_alpha-2):

PREFIX gro: <https://globalresearchopportunities.northwestern.edu/ontology/gro.owl#>
PREFIX content: <http://purl.org/rss/1.0/modules/content/>
PREFIX dc: <http://purl.org/dc/terms/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>

SELECT distinct ?subject ?object ?title ?hp WHERE
{ ?subject rdf:type gro:Opportunity .
 ?subject gro:countrycode "UK" .
 ?subject content:encoded ?object .
 ?subject dc:title ?title .
 ?subject foaf:workInfoHomepage ?hp }

Useful links:
- Syntax tutorial
- Statistic queries

RDF Mapping in Opportunities

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User

RDF Types: foaf:Person

International Affiliate / External Partner

RDF Types: sioc:Item, foaf:Document, gro:Partner ()

Opportunity

RDF Types: sioc:Item, foaf:Document, obo:ERO_0000595, gro:Opportunity ()

Taxonomy Terms

All Taxonomy terms share the same predicates, but have unique classes.

Tags

RDF Types: skos:Concept, gro:Tags

Affiliate Type

RDF Types: skos:Concept, gro:AffiliateType

Campus

RDF Types: skos:Concept, vivo:Campus, gro:Campus

Course Credit Availability

RDF Types: skos:Concept, gro:Credit

Funding Availability

RDF Types: skos:Concept, gro:FundingAvailability

Keywords

RDF Types: skos:Concept, gro:Keywords

Languages

RDF Types: skos:Concept, vcard:Language, gro:Language

NU Center Program Institute

RDF Types: skos:Concept, gro:NUCenterProgramInstitute

NU Department

RDF Types: skos:Concept, vivo:Department, gro:Department

NU Partner

RDF Types: skos:Concept, gro:NUPartner

NU School

RDF Types: skos:Concept, vivo:School, gro:School

Opportunity Country

RDF Types: skos:Concept, vivo:GeographicLocation, gro:Country

Opportunity Direction and Mapping Options

RDF Types: skos:Concept, gro:Direction

Opportunity Type

RDF Types: skos:Concept, gro:OpportunityType

Participants

RDF Types: skos:Concept, gro:Participant

Primary Subject Area

RDF Types: skos:Concept, gro:SubjectArea

Regions

RDF Types: skos:Concept, vivo:GeographicRegion, gro:Region

Subject Discipline

RDF Types: skos:Concept, gro:SubjectDiscipline

Timeframes

RDF Types: skos:Concept, gro:Timeframe

Topics

RDF Types: skos:Concept, gro:Topic

Type of Payment

RDF Types: skos:Concept, gro:Payment

Action

RDF Types: skos:Concept, gro:Action

Namespaces

Namespaces are the full length name of the various prefixes.