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Motivation / Introduction

Rough Sets for Informative Question Answering Julia A. Johnson and Mengchi Liu University of Regina { johnson, mliu } @ cs.uregina.ca. Motivation / Introduction. When it is not possible to give a precise answer, it may be possible to give an imprecise answer which is nevertheless,

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Motivation / Introduction

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  1. Rough Sets for InformativeQuestion AnsweringJulia A. Johnson and Mengchi LiuUniversity of Regina{ johnson, mliu } @ cs.uregina.ca

  2. Motivation / Introduction When it is not possible to give a precise answer, it may be possible to give an imprecise answer which is nevertheless, informative.

  3. Outline • Definition of Rough Set. • Table of possible answers using Rough Sets. • Algorithm and definitions of Belief and Plausibility. • Example of computation of B and P. • Comparison of B and P to provide informative answers. • Conclusions.

  4. The lower approximation is X =  i whereix The upper approximation is X= i where (i x)

  5. Informative question answering with rough sets(1) a ‘No’ answer (2) an uncertain answer (3) a precise answer (4) an uncertain answer

  6. Based on the attributes minimal set for information table if ((X- X) =) then the sick people are precise answer else the sick people include X and that is B % of all the people and are included in X and that is P % of all the people.

  7. X * 100 X1 + X2 ,…, Xn where B = P = X * 100 X1 + X2 ,…, Xn

  8. = B = sick (total number of examples) * 100 2  8 * 100 = 25% B tells the user the proportion of the people we know are definitely sick. P = sick  (total number of examples) * 100 6  8 * 100 = 75% P tells the user the proportion of the people who we know are possibly sick. =

  9. P - B P It is also useful for informative question answering to compare the values of B and P. If B ~ P then our belief is high that the uncertain answer if fairly precise. If < 0.1 then the sick people include { a printed list of the examples of X } and there are very few additional sick people.

  10. Conclusions Usage of the rough set model has been illustrated through its use in informative question answering. The rough set model was used for distinguishing between precise answers that say “There are no objects X” or “The objects X are…”, from uncertain answers of the form “Objects X are included in set Y” or “There may be additional objects X other than those in set Y”.

  11. Conclusions These uncertain answers offer an improvement over the more traditional precise answers because the system is better able to report on its “lack of knowledge”. The answer “There are no objects X” is not given when the answer “I do not know” should be given.

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