The W3C RIF Working Group has just published the RIF specification as a Candidate Recommendation. As a coincidence, the OWL 2 Working Group published the OWL 2 specification as Proposed Recommendation just a few days before. Ie, two major sets of technologies that can be used for various kinds of inferences on the Semantic Web have arrived to a high level of maturity almost at the same time. If everything goes as planned (I know, it never does, but one can still speculate) they will become Recommendations around the end of the year.

A group of questions I often get is: how do these two sets of recommendations relate to one another? Did W3C create competing, incompatible technologies that are in the same design space? Why having two? How can they be combined?

To answer this question one has to realize that the two sets of technologies represent different approaches. OWL 2 (and, actually, RDFS) rely, very broadly speaking, on knowledge representation techniques. Think of thesauri, of ontologies, of various classification mechanisms: one classifies and characterizes predicates, resources, and can then deduce logical consequences based on that classification. (On the Semantic Web this usually means discovering new relationships or locating inconsistencies.) RIF, on the other hand, is more reminiscent of logic programming (think of Prolog). Ie, if these and these relationships hold then new relationships can be deduced. (It must be said that RIF also includes a separate work on production rules, but they are fairly distinct from OWL 2, so let us forget about that for the moment.)

Would I want to use OWL 2 or rather RIF to develop an application? Well, it depends. Some applications are better formulated this way, others that way. There are a number of papers published on when one approach is better than the other, how certain tasks can or cannot be expressed using classification or rules, respectively, how reasoning is possible in one circumstances or the other. Very often it also boils down to personal experience and, frankly, taste: some feel more comfortable using rules while others prefer knowledge representation. I do not think it makes sense to claim that one is better than the other. Simply put: they are different and both approaches have their roles to play.

So far so good, the reader could say, but what about using OWL and RIF together?

One of the six recommendation track documents of RIF is called “RIF RDF and OWL Compatibility”. Because we are talking about formal semantics, this document is of course not an easy read. However, in layman's term, what it describes is how the two “sides”, ie, the rule and the classification sides, should work together on the same data set. It defines some sort of an interplay between two different mechanisms: the, shall we say, logic programming part and the knowledge representation part. Implementations doing both are a bit like hybrid cars: they have two parallel engines and a well defined connections between those two. That said, the document only defines what the combination means; whether, for example, engines will always succeed in handling the two worlds together in a finite time is not necessarily guaranteed in all cases. But we can be positive: in many cases (ie, by accepting restrictions here and there) this combination does work well, and there are, actually, good implementations out there that do just that.

A simple case where no problem occurs is the so called OWL 2 RL Profile. This profile has been defined by the OWL Working Group with the goal of being implementable fully via rule engines. This does not necessarily means RIF (I myself have implemented OWL 2 RL by direct programming in Python), but the fact that RIF could also be used is important. The RIF Working Group has therefore published a separate document (“OWL 2 RL in RIF”) which shows just that: it reformulates the rules for the implementation of OWL 2 RL as RIF rules (more exactly, RIF Core rules). Ie, a RIF implementation can just take those rules, import any kind of RDF data that also include OWL 2 statements, and the RIF engine will produce just the right inference results. How cool is that?

So, the answer to the original question is: yes, for many applications, RIF and OWL 2 can happily live together ever after…




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