David, thanks. Like the examples. Only thing to make sure is that the data/graph used to draw the conclusion is not altered with back-dated data - think this aspect is already being discussed.
Cheers Sivaram On Wednesday, January 16, 2013, David Booth wrote: > Hi Siviram, > > On Wed, 2013-01-16 at 14:51 -0600, Sivaram Arabandi, MD wrote: > > I am enjoying reading and catching up on this thread. > > > > David, you mentioned 'rdf model' below - are you referring to ontology > > models? > > Yes, sort of. One can design an RDF model without formalizing it into a > written ontology. So I was referring to the schema of the RDF data -- > classes, relationships, etc -- whether or not that schema is implicit or > explicit (i.e., written into an ontology). > > > And, you said "To my mind, monotonicity is the key." But in medicine > > most reasoning is non-monotonic - default reasoning, (educated) > > guesses and revision of diagnosis as new data comes into the picture. > > What am I missing here? > > Today you might conclude "As of 16-Jan-2013 the diagnosis is X", but > tomorrow you might conclude "As of 17-Jan-2013 the diagnosis is Y". If > you represent the statements that way (qualified by the particular > context or, in this case, date) then they are monotonic -- they remain > true forever, regardless of what new information arrives. Whereas if > today you were to represent that information simply as "The diagnosis is > X" then it would be non-monotonic, because tomorrow you might need to > change it to "The diagnosis is Y". > > OTOH, even if the data is monotonic, you can cleanly use default > reasoning and the closed world assumption in the way you *use* that > data. For example, if the data is represented like "As of 16-Jan-2013 > the diagnosis is X", then a query can return the *latest* diagnosis and > report that "the current diagnosis is X". Tomorrow, after more data has > been added, that same query might report that "the current diagnosis is > Y". > > You can think of "the current diagnosis is X" as being derived, > non-monotonic data. If you store it, you must be careful to treat it > only as cached information that will be invalidated when its antecedent > information changes. The RDF Pipeline framework that I've been working > on > http://code.google.com/p/rdf-pipeline/ > was designed in part to handle this kind of problem: to keep track of > information dependencies and update derived non-monotonic information > automatically. Here are slides about it from last year's Semantic > Technology conference: > http://dbooth.org/2011/pipeline/ > > You can also design your RDF data models so that certain pieces make > closed world assumption or use defaulting conventions. Then you have to > be careful to keep track of which pieces they are, so that when you > merge data you do so appropriately without invalidating anything. For > example, you might change defaults into explicit values before merging. > > > -- > David Booth, Ph.D. > http://dbooth.org/ > > Loss of web prodigy Aaron Swartz: http://tinyurl.com/ahe2k8c > > Opinions expressed herein are those of the author and do not necessarily > reflect those of his employer. > > -- ______________________ Sivaram Arabandi, MD, MS Ph: 832.726.2322