Hi,
Looking further for commonalities with the OWL world… What would be a natural way of modelling relationships that would behave as symmetric, inverse and transitive OWL properties?
The OWL migration page in Grakn documentation is not exactly clear about this… 
Thanks,
Marcel
Hi Marcel,
In general the inference rule mechanism is exactly what you need to express different meta-properties over relationships in your data, such as those you mention. We’re still working on it and it’s not properly documented on our website yet, but take a look our blog post for some quick examples: https://blog.grakn.ai/how-to-find-information-that-is-not-there-de0d66360179#.jzsbnqkzx
Note also that in case of symmetric relations there’s also more straightforward way. Basically, our n-relations are unordered tuples of role-role-player pairs. So if you define a symmetric relation, say neighbouring, with only one role neighbour, and assert:
(neighbour: person1, neighbour:person2) isa neighbouring
and query for:
match (neighbour:$x, neighbour:$y) isa neighbouring
you will get two matches:
person1, person2
person2, person1
which is exactly what you would expect from a symmetric relation.
Szymon