Triply

TriplyCreated on Sep 17th, 2019
Members
1.749 statements

Triply has converted the famous Iris flower dataset to linked data! It is a multivariate dataset that quantifies the morphologic variation of Iris flowers of three different species, measured in four different properties. In this data cube, each species of Iris occurs 50 times and this linked data version uses the RDF Data Cube Vocabulary.

How to start a SPARQL service

TriplyDB allows you to expose your dataset through SPARQL. Exposing your data via SPARQL gives you the opportunity to create SPARQL queries and datastories over your own dataset or datasets from others. On TriplyDB you can already find a several examples of SPARQL queries. But creating your own SPARQL queries requires you to first start a SPARQL service over your dataset. The following step by step guide helps you do to start a SPARQL service.

  1. Go to the Services page and you'll see a form to create a SPARQL service.
  2. To Create a SPARQL service you fill in a name for your service and select SPARQL from the three options.
  3. Press Create service to confirm your choices and a SPARQL service will be started.
  4. Wait until the status of the service changed to running.
  5. A new option called SPARQL will appear in the sidebar. Clicking the button opens the SPARQL editor where you can write queries over your dataset.

How to import DBpedia

The iris dataset reuses classes, properties and resources from DBpedia. This not only reduces the amount of maintenance, but by reusing objects from DBpedia we can make use of the links that DBpedia already created. But before you can use the objects from DBpedia you'll first need to import DBpedia into the Iris dataset. The following step by step guide helps you do exactly that.

  1. Go to the Graphs page and click on import a new graph
  2. Click on Add data from an existing dataset
  3. Type in DBpedia and select DBpedia-association / dbpedia from the dropdown menu.
  4. The page should now change and there is now one graph selected. This graph consist out of 369.205.380 statements and is the full DBpedia dataset.
  5. To import this into your dataset you can click import 1 graphs. This will add the DBpedia graph into your dataset.
  6. You've now imported the DBpedia graph into your dataset. You can now use the browser and see more information about DBpedia resources.
  7. To remove the DBpedia graph from your dataset you can go the graphs page and remove the dataset by clicking on the X behind the https://triplydb.com/wikimedia/dbpedia/graphs/default graph. This will remove your local connection to DBpedia.

PS: It is not allowed to start or sync a service when DBpedia is added as a graph. To start a service you will first need to remove the DBpedia graph by following step 7.

Metadata for YALC (Yet Another LOD Cloud), a registry of Linked Open Datasets.

IMDb is an online database of information related to world films, television programs, home videos and video games, and internet streams, including cast, production crew, personnel and fictional character biographies, plot summaries, trivia, and fan reviews and ratings.

Version: 1.0.0

A vocabulary for representing compound values, i.e., complex values that are composed out of one or more atomic values. Such values are not supported by the current Linked Data standards.

This vocabulary allows gepspatial datasets and services to be described.

Explanation of this vocabulary

This vocabulary reuses DCAT GeoSPARQL and adds specializations and extensions on top of this.

The following diagram shows how the classes and properties in this vocabulary interact:

A datast containing instances for the media types in the IANA registry.

Media types and the IANA registry are standardized in RFC 6838.

How to use this dataset

This dataset provides instances for media types. You can use the following existing properties in order to relate these media types to your resources:

dct:format
This is the standard way to denote a media type according to the Dublin Core Terms vocabulary.
sdo:encodingFormat
This is the standard way to denote a media type according to the Schema.org vocabulary.

Explanation of the data model

The following diagram gives an overview of the data mode for this dataset:

This dataset uses the following existing class and property from Dublin Core Terms:

dct:MediaType
Every media type is an instance of this class.
dct:identifier
The identifier for each media type and top-level type.

This dataset introduces the following new class and properties:

tmt:ToplevelType
Every top-level type is an instance of this class.
tmt:altExtension
Specifies a file name extension that is sometimes used on files with content in this particular media type. This is not the preferred file name extension to use of files with content in this particular media type (see tmt:prefExtension).
tmt:prefExtension
Specifies the preferred file name extension for a particular media type.
tmt:toplevelType
Relates a media type to its corresponding top-level type.
tmt:subtype
Relates a media type to its subtype name.

This vocabulary allows versions to be identified for arbitrary resources.

Explanation of this vocabulary

This vocabulary innovates over current ontologies, which only allow OWL ontologies to be versioned (OWL), and/or only allow versions to be expressed in simple numbers or strings (Schema.org).

This vocabulary introduces the following two version types:

Version instances are related to the resources with the following property:

This vocabulary allows prefix declarations to be represented for arbitrary resources.

Explanation of this vocabulary

This vocabulary innovates over current vocabularies, which only allow one single prefix declaration to be defined (VANN), and/or only allow prefix declarations to be asserted for ontologies (SHACL, VANN).

At the same time, this vocabulary does reuse those aspects of SHACL and VANN that are generic enough to be reused here. From SHACL the following class and properties are reused:

From VANN the following properties are reused:

The following property is newly introduced by this vocabulary:

The following diagram shows how these classes and properties interact:

Use cases for this vocabulary

Examples of resource types for which prefix declarations are commonly asserted include:

Datasets
Prefix declarations to abbreviate IRIs that appear in the dataset.
Queries
Prefix declarations used in (collections of) SPARQL queries.
Rules
Prefix declarations that are used in (collections of) SHACL rules.
Services
Prefix declarations that can be used to query service endpoints.

Relation to sh:declare

At Triply we our policy is to not publish direct assertions about nodes published by other organizations, but the intention of the property introduced by this vocabulary is to act as a superproperty of sh:declare:

prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
prefix sh: <http://www.w3.org/ns/shacl#>
prefix tp: <https://triplydb.com/Triply/tp/def/>

sh:declare rdfs:subPropertyOf tp:prefixDeclaration.

A simple dataset that uses Data Cube.