This is part three of Comparing Semantic Web Technologies to Grakn. In this part, we look specifically at OWL and SHACL.
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OWL is a family of Descriptive Logic (DL) based ontology language which adds ontological constructs on top of RDFS to express conditions and derive new facts. To make meaningful use of these, OWL provides different flavours: OWL QL, OWL RL, OWL DL, with the user needing to decide which fits their use case best.
Grakn, on the other hand, comes with its own built-in native inference capabilities. This is an important distinction, because OWL assumes a good understanding of the field of logic by the user, while using Grakn doesn’t require the user to have studied this field extensively.
The result is that OWL struggles to maintain a satisfactory balance between expressiveness and complexity. Just to reason over two simple constraints: Every parent has a child and Every child is a person requires the use a fully-fledged OWL DL reasoner. Furthermore, OWL is not suited to reasoning with complex relations. Its formal foundations, based on a tree-model property, make it a better fit for tree-shaped data, but scale poorly with more complex data.
OWL adopts open world assumptions, instead of Grakn’s closed world assumptions. This means that in an example where OWL has the constraint: Every parent must have at least one child, if we have a person with no children, this is still consistent with the constraint, as we may not yet know about John’s children. However, with Grakn’s closed world assumption, if there are no actual mentions of John’s children, this means he really doesn’t have any children and isn’t a parent.
Open world assumptions lend themselves well for the open-ended web, which includes incomplete information from multiple sources, which is why OWL provides many concepts to manage and deal with this incompleteness. However, because of this open world assumption, OWL makes it hard to validate consistency of data. That’s why relational databases maintain schema constraints to guarantee the quality of data. Grakn combines both styles of reasoning: ontological style open world inference, and schema like closed world constraint checking.
Everything put together, OWL has a very high entry threshold for non-logicians. As it’s based on description logic, developers avoid using OWL as it’s non-trivial to understand the language and its intended behaviour. Because of this, Grakn’s knowledge representation formalisms remain lightweight, providing semantic capabilities to a much larger audience than that of OWL. In other words, Grakn is simpler to use than OWL.
Here we look at some common axioms in OWL and comapre them to Grakn. This is not an exhaustive list, and is provided to help the user understand how to think of migrating to Grakn.
A key functionality of OWL is to define restriction classes ( owl:Restriction). These unnamed classes are defined based on restrictions of the values for certain properties of the class. OWL allows to model situations where certain members of a class need to have certain properties.
