Databases Reference
In-Depth Information
=
=
Ta b l e 4 .
Example accuracies for selected cases (eq
equivalence class axiom, sc
super class
axiom). The images on the left represent an imaginary knowledge base
with 1000 individuals,
where we want to describe the class
A
by using expression
C
. It is apparent that using predictive
accuracy leads to impractical accuracies, e.g. in the first row
C
cannot possibly be a good descrip-
tion of
A
, but we still get 80% accuracy, since all the negative examples outside of
A
and
C
are
correctly classified.
K
illustration
pred. acc. F-Measure A-Measure Jaccard
eq
sc
eq
sc
eq
sc
80% 67%
0%
0%
0%
0%
0%
90% 92% 67% 73% 75%
88%
50%
70% 75% 40% 48% 63%
82%
25%
98% 97% 90% 90% 90%
90%
82%
95% 88% 67% 61% 75%
63%
50%
The Protégé Plugin.
After implementing and testing the described learning algorithm,
we integrated it into
Protégé
and
OntoWiki
. Together with the Protégé developers, we
extended the Protégé 4 plugin mechanism to be able to seamlessly integrate the DL-
Learner plugin as an additional method to create class expressions. This means that
the knowledge engineer can use the algorithm exactly where it is needed without any
additional configuration steps. The plugin has also become part of the o
cial Protégé 4
repository, i.e. it can be directly installed from within Protégé.
A screenshot of the plugin is shown in Figure 30. To use the plugin, the knowledge
engineer is only required to press a button, which then starts a new thread in the back-
ground. This thread executes the learning algorithm. The used algorithm is an
anytime
algorithm
, i.e. at each point in time we can always see the currently best suggestions.
The GUI updates the suggestion list each second until the maximum runtime - 10 sec-
onds by default - is reached. This means that the perceived runtime, i.e. the time after
which only minor updates occur in the suggestion list, is often only one or two seconds
for small ontologies. For each suggestion, the plugin displays its accuracy.
