Dear all, I am planning to use Jena/Fuseki as backend for my application and wonder what the best way is to get data inference. I have a data model (based on an ontology) where (for instance) there is a “Status” class that can have many levels of sub-class, like Status->Superior->Abbot or Status->Superior->Prior and so on. I would like to use this in SPARQL queries where I can ask for individual instances of the main class as well as any subclass. For this I would like to use inference based on my Ontology (or better, Ontologies). The data will change frequently as it will be connected to an online editor where individual instances can be created or modified.
As far as I can tell I have two options: * Configure the inference in the software with Jena using ModelFactory, ReasonerRegistry and so on Advantages: The data is always up-to-date and based on the current version of the ontologies exposed via URL Disadvantages: Slow as the model has to be built for each request (is this correct?) * Configure the inference for Fuseki by using a Dataset for the live data, one for the ontologies (they have to be stored in Fuseki?) and a combined dataset configured to use inference for the union of both other datasets. Advantages: faster Disadvantages: It’s more difficult to update the data or ontologies Are these assumptions correct? Is there a way to speed up the inference configured in Jena directly? What would be the preferred way to realize such a service? Any advice is greatly appreciated. Best, Daniel -- Mag. Daniel Jeller Monasterium / Digitisation / IT ICARUS -- Spaces Central Station Gertrude-Fröhlich-Sander Str. 2-4 Tower C, Floor 7-9 A-1100 Vienna AUSTRIA Web: http://icar-us.eu<http://icar-us.eu/> Platforms: http://monasterium.net<http://monasterium.net/>, http//matricula.info Join the ICARUS friends‘ association: http://4all.icar-us.eu<http://4all.icar-us.eu/>
