7 IR models based on predicate logic
7 IR models based on predicate logic
7 IR models based on predicate logic
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<str<strong>on</strong>g>IR</str<strong>on</strong>g> <str<strong>on</strong>g>models</str<strong>on</strong>g> <str<strong>on</strong>g>based</str<strong>on</strong>g> <strong>on</strong> <strong>predicate</strong> <strong>logic</strong> 25<br />
7.4.2 Semantics of probabilistic Datalog<br />
Extensi<strong>on</strong>al vs. intensi<strong>on</strong>al semantics<br />
0.9 docTerm(d1,ir).<br />
0.5 docTerm(d1,db).<br />
0.7 link(d2,d1).<br />
about(D,T) :- docTerm(D,T).<br />
about(D,T) :- link(D,D1) & about(D1,T)<br />
q(D) :- about(D,ir) & about(D,db).<br />
extensi<strong>on</strong>al semantics:<br />
weight of derived fact as functi<strong>on</strong> of weights of subgoals<br />
P (q(d2)) = P (about(d2,ir)) · P (about(d2,db)) =<br />
(0.7 · 0.9) · (0.7 · 0.5)<br />
Problem:<br />
“improper treatment of correlated sources of evidence”<br />
[Pearl]<br />
→ extensi<strong>on</strong>al semantics <strong>on</strong>ly correct for tree-like<br />
inference structures<br />
intensi<strong>on</strong>al semantics:<br />
weight of IDB fact as functi<strong>on</strong> of weights of underlying<br />
ground facts<br />
Norbert Fuhr
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