yy20716 / mondo-hpo-learner Goto Github PK

Use either ncit.owl or neoplasm.owl (@balhoff to advise)

unlike with the other experiments, there is no separate associations file. Everything is axiomatized within NCIT. The goal is to find in an unbiased way which object properties work best for classifying diseases.

dllearner won't work out the box, it uses instances as training data. We will want to materialize instance graphs for each subClassOf disease. There is some code in owltools that can be used but it may be better to write clean code in scala (or sparql?).

For each disease class

1. weaken existing equivalence axioms to subClassOf
e.g. C = G1 and R1 some Y2 and R2 some Y2 .. ==>
C subClassOf G1, R1 some Y1, ...

2. for each subClassOf axiom, translate recursively to object property assertions.

E.g. if we have

C subClassOf R some D
D subClassOf D2
D2 SubClassOf R2 some E..
==>
i1 a C
i1 R i2
i2 a D
i2 R2 i3
i3 a E

this would form the instance graph input