This year me and a friend have to make a project for the final year of university. The plan is to make a proxy/sever that allows to store ontologies and RDF’s, by this way this data is “chained” to a web, so you can make a request for that web and the proxy will send you the homepage with metadata.
We have been thinking to use python and rdflib, and for the web we don’t know which framework is the best. We thought of django, but we think that is very big for our purpose, and we decided that webpy or web2py is a better option.
We don’t have any python coding experience, this will be our very first time. We have been always programming in c++ and java.
So taking into account everything we’ve mentioned our question is, which would be the best web framework for our project? And will rdflib suit fine with this framework?
Thanks 🙂
I have developed several Web applications with Python framworks consuming RDF data. The choice always depends on the performance needed and the amount of data you’ll have to handle.
If the number of triples you’ll handle is in the magnitude of few thousands then you can easily put together a framework with RDFlib + Django. I have used this choice with toy applications but as soon as you have to deal with lots of data you’ll realise that it simply doesn’t scale. Not because of Django, the main problem is RDFlib’s implementation of a triple store – it is not great.
If you’re familiar with C/C++ I recommend you to have a look at Redland libraries. They are written in C and you have bindings for Python so you can still develop your Web layer with Django and pull out RDF data with Python. We do this quite a lot and it normally works. This option will scale a bit more but won’t be great either.
In case your data grows to millions of triples then I recommend you to go for a Scalable Triple store. You can access them through SPARQL and HTTP. My choice is always 4store. Here you have a Python client to issue queries and assert/remove data 4store Python Client