I'm planning a system that combines various data sources and lets users do simple queries on these. A part of the system needs to act as an abstraction layer that knows all connected data sources: the user shouldn't [need to] know about the underlying data "providers". A data provider could be anything: a relational DBMS, a bug tracking system, ..., a weather station. They are hooked up to the query system through a common API that defines how to "offer" data. The type of queries a certain data provider understands is given by its "offer" (e.g. I know these entities, I can give you aggregates of type X for relationship Y, ...).
My concern right now is the unification of the data: the various data providers need to agree on a common vocabulary (e.g. the name of the entity "customer" could vary across different systems). Thus, defining a high level representation of the entities and their relationships is required.
So far I have the following requirements:
I need to be able to define objects and their properties/attributes. Further, arbitrary relations between these objects need to be represented: a verb that defines the nature of the relation (e.g. "knows"), the multiplicity (e.g. 1:n) and the direction/navigability of the relation.
It occurs to me that RDF is a viable option, but is it "the right tool" for this job?
What other solutions/frameworks do exist for semantic data modeling that have a machine readable representation and why are they better suited for this task?
I'm grateful for every opinion and pointer to helpful resources.
