GoodRelations is a standardized vocabulary (also known as “schema”, “data dictionary”, or “ontology”) for product, price, store, and company data that can (1) be embedded into existing static and dynamic Web pages and that (2) can be processed by other computers. This increases the visibility of your products and services in the latest generation of search engines, recommender systems, and other novel applications.

Version: 1.0.0

• SPARQL endpoint

The Gene Ontology resource provides a computational representation of our current scientific knowledge about the functions of genes (or, more properly, the protein and non-coding RNA molecules produced by genes) from many different organisms, from humans to bacteria. It is widely used to support scientific research, and has been cited in tens of thousands of publications.

Understanding gene function—how individual genes contribute to the biology of an organism at the molecular, cellular and organism levels—is one of the primary aims of biomedical research. Moreover, experimental knowledge obtained in one organism is often applicable to other organisms, particularly if the organisms share the relevant genes because they inherited them from their common ancestor. The Gene Ontology (GO), as a consortium, began in 1998 when researchers studying the genome of three model organisms—Drosophila melanogaster (fruit fly), Mus musculus (mouse), and Saccharomyces cerevisiae (brewer’s or baker’s yeast)—agreed to work collaboratively on a common classification scheme for gene function, and today the number of different organisms represented in GO is in the thousands. GO makes it possible, in a flexible and dynamic way, to provide comparable descriptions of homologous gene and protein sequences across the phylogenetic spectrum.

GO is also at the hub of a major effort to represent the vast amount of biomedical knowledge in a computable form. GO is linked to many other biomedical ontologies, and is a foundation for research applying computer science in biology and medicine.

Version: 1.0.0

• SPARQL endpoint

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 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.