On Feb 17, 2011, at 3:23 PM, Shawn Bowers wrote:
Both OBOE and EQ do introduce classes that prescribe how to structure new classes and type individuals
That's actually not quite true. The EQ model itself doesn't prescribe any new classes or the types that individuals must be of; instead it simply says that a phenotype instance can be expressed as some instance of a quality Q that inheres_in some instance of an entity E, and thus a class of phenotypes (or observations of an organism's characteristics) is the intersection of all instances of Q (a subclass restriction), and all things that inhere_in E (a property restriction).
While typically we will draw Q and E from certain ontologies (such as PATO for qualities), you can designate any class (term) in those places, and the class expression by itself will not support inferences about the nature of Q or E or their instances (the ontologies that Q and E are drawn from do that). The class expression itself is often anonymous, but there are (so-called "pre-composed") ontologies that identify and label them.
That being said, while EQ in principle allows you to do real crazy things if you want to (which perhaps is what Joel means by schema- last?), if you want to be able to do discovery and reasoning with a set of EQ class expressions from different sources, they will need to follow some shared conventions, such as not simply making up quality and entity terms as needed, but drawing them from PATO and shared entity ontologies.
Conversely, OBOE does prescribe the nature of the things that it relates to each other in the model, the cardinality of those relationships, and what it means for an instance it is has such a relationship. For example, if I assert o oboe:ofEntity e, the semantics of oboe:ofEntity prescribe that o is an instance of oboe:Observation, e is an instance of oboe:Entity, and if I also assert o oboe:ofEntity e1, it prescribes that e and e1 are identical, i.e., the same instance.
I think these differences are a result of how they were motivated, and it is interesting to me that Joel would pick these as examples for illustrating "schema-lastishness". OBOE was motivated by having a unified data model for observational data, in the interest of better data exchange and integration. I think all its class and property constraints are a reflection of that - there is a desire not to "allow anything". Conversely, EQ wouldn't make for a good model in which to exchange arbitrary observational data - there would be no guarantees for what you get. However, it is very powerful for reasoning over the semantics of the observations (see the Washington et al 2009 paper), which is what it was conceived for.
On Thu, Feb 17, 2011 at 11:28 AM, joel sachs jsachs@csee.umbc.edu wrote:
Do you (or does anyone else on the list) know the status of OBD? From the NCBO FAQ:
Funny you should ask. We're in the final stages of writing up a manuscript about it. I can share a preprint with you next week. OBD is what is underpinning the Phenoscape Knowledgebase (http://kb.phenoscape.org ).
The URL is http://www.berkeleybop.org/obd/. It is still pretty outdated, but will be updated very soon.
Is it still the plan to integrate OBD into BioPortal?
I don't think so. And there are lots of resources working on that (at least in the biomedical domain), so it'd be hard for them to pick what to follow.
So in the OBOE case, the characteristics (color, perimeter texture, basic shape) are given a priori, while in the EQ case they would (presumably) be abstracted during subsequent ontology development.
Yes. They are implied by the subclass structure of PATO (and thus subject to change).
it might be worth experimenting with tag-driven ontology evolution, as in [1], where tags are associated to concepts in an ontology. [...] So the domain expert/knowledge engineer partnership is preserved, but with the domain expert role being replaced by collective wisdom from the community.
Are you aware of the "Fast, Cheap, and Out of Control" paper from Mark Wilkinson's group: Good et al. 2006. Fast, Cheap and Out of Control: A Zero Curation Model for Ontology Development. Pacific Symposium on Biocomputing 11: 128-139.
http://psb.stanford.edu/psb-online/proceedings/psb06/good.pdf
-hilmar