Information Retrieval as Counterfactual

JianYun Nie
Martin Brisebois
Dpartement d'informatique et de recherche oprationnelle
Universit de Montral
C.P. 6128, succursale Centreville
Montral, P.Q
H3C 3J7, Canada
phone: (514) 3432263
fax: (514) 3435834
email: nie@iro.umontreal.ca
briseboi@iro.umontreal.ca
Franois Lepage
Dpartement de philosophie
Universit de Montral
C.P. 6128, succursale Centreville
Montral, P.Q
H3C 3J7, Canada
email: lepagef@ere.umontreal.ca

Relevance, one of the fundamental notions in Information Retrieval (IR),
has long been studied from a cognitive point of view. It is known that
relevance depends not only on the topic of the document and the
information need expressed in a query, but also on other "situational"
factors in the retrieval situation, such as the user's previous knowledge,
background, intentions, and so on. Formal models, on the other hand,
usually consider relevance from a system point of view, i.e. they isolate
relevance in a restricted context in which only the topic matters. One of
the reasons for this very partial modeling is due to the inappropriateness
of standard formal tools for describing relevance in a general context.
This paper is an attempt to identify a more appropriate logical framework
for the modeling. Counterfactual conditional logic is examined with
respect to the IR requirements, indicating the logic's high potential value
for this task. A particular conditional logic is then defined which, in
comparison with previous developments on conditional logic, is better
suited to IR. The new model gives an insight into the phenomena related
to the "situational" factors of relevance judgment which, until now, have
not been considered.



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