7 IR models based on predicate logic

<strong>IR</strong> <strong>models</strong> <strong>based</strong> on predicate logic 33
Probabilistic rules
rules for deterministic facts:
0.7 likes-sports(X) :- man(X).
0.4 likes-sports(X) :- woman(X).
man(peter).
interpretation:
P (W 1 ) = 0.7: {man(peter),
likes-sports(peter)}
P (W 2 ) = 0.3: {man(peter)}
rules for uncertain facts:
# sex(dk,av).
0.7 l-s(X) :- sex(X,male).
0.4 l-s(X) :- sex(X,female).
0.5 sex(X,male) :- human(X).
0.5 sex(X,female) :- human(X).
human(peter).
interpretation:
P (W 1 ) = 0.35: {sex(peter,male), l-s(peter)}
P (W 2 ) = 0.15: {sex(peter,male)}
P (W 3 ) = 0.20: {sex(peter,female), l-s(peter)}
P (W 4 ) = 0.30: {sex(peter,female)}
Norbert Fuhr