Guided tour

The first stage in getting started with the PROPheT tool is to download and install it. This can be done by following the very easy to follow instructions at:

http://mklab.iti.gr/prophet/install.html

At present, this PROPheT can only be installed and run using a Windows PC.

 

Once the software is installed, it is now time to get started with using PROPheT and exploring its functionality. The best way to do this is to follow the first part of the tutorial (http://mklab.iti.gr/prophet/demonstration.html#general-functions) which enables the user to get to know the tool step-by-step. First steps of using the tool include:

  • Become familiar with the Main Window – this is the ‘home’ space for the user, from where they perform other functions.
  • Then Load an ontology – before being able to explore an ontology, you first need to open it up or load it in to PROPheT.
  • Finally, View content of ontology – this is where it begins to get interesting for the user – what is in the ontology?

Having viewed and explored the contents of an ontology in PROPheT, we can now explore its features:

  • In order to incorporate information from an external model, the user must Select an endpoint (with SPARQL) which can then be queried – an example used in the tutorial is the Linked Movie Database (http://www.linkedmdb.org/)
  • Define general parameters – for the functioning of PROPheT, such as how many instances to derive from an external model.
  • The use of Known namespaces simplifies the identification and naming of ontologies used by the tool – for example a long and difficult to read URI can be names ‘ my_ont’.
  • Users have total control over external models that are incorporated into PROPheT, and so the Ontology mapping is user-driven – here the user can ensure that all previous values attached to an instance in an external resource are relevant for their model.
  • PROPheT’s database is used to store all information relating to the user’s model.
  • The Export populated ontology can be used to export or store the user’s model to a local file (e.g., .owl, .rdf).
  • The user’s ability to View log entries ensures that they can monitor all the important actions that have been undertaken with their ontology model – this ensures that users have control over their data.