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          An Extended Fuzzy Linguistic Approach to
          Generalize Boolean Information Retrieval


                      Donald H, Kraft
           Department of Computer Science (Chairman)
                 Louisiana State University
                Baton Rouge, LA  70803-4020
                            USA
                   (504) 388-1495  Voice
                    (504) 388-1465  Fax
                   kraft@bit.csc.lsu.edu

                ** Wednesday, Oct 14, 1992  4 PM **
                   ** Manning Hall, Room 117 **


Abstract


     The generalization  of  Boolean  information  retrieval
systems (IRS) is still of interest to scholars.  In spite of
the fact  that  commercial  systems  use  Boolean  retrieval
mechanisms, such systems still have some limitations. One of
the main problems is that such systems lack the  ability  to
deal  well  with  imprecision  and  subjectivity.   Previous
efforts have led to the introduction of numeric  weights  to
improve  both  document  representations  (term weights) and
query  languages  (query  weights).   However,  the  use  of
weights  requires  a clear knowledge of the semantics of the
query in order to translate a fuzzy concept into  a  precise
numeric  value.   Moreover,  it  is  difficult  to model the
matching of queries to documents in a way that will preserve
the semantics of user queries.

     A linguistic extension  has  been  generated,  starting
from  an  existing Boolean weighted retrieval model and for-
malized within fuzzy set  theory,  in  which  numeric  query
weights are replaced by linguistic descriptors which specify
the degree of importance of the terms.

     In the past, query weights were seen as measures of the
importance  of  a specific term in representing the query or
as a threshold to aid in matching a specific document to the
query.   The  linguistic extension was originally modeled to
view the query weights as a description of the  ideal  docu-
ment,  so  that deviations would be rejected whether a given
document had term weights that were too  high  or  too  low.
This  paper  looks  at  an extension to the linguistic model
that is not symmetric in that documents with a  term  weight
below  the  query  weight are treated differently than docu-
ments with a term weight above the query weight.