Personalizing Information
Retrieval in CRISs with
Fuzzy Sets and Rough Sets
Germán Hurtado Martín1,2
Chris Cornelis2
Helga Naessens1
1. University College Ghent, 2. Ghent University (Belgium)
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Overview
 Problems
in CRISs
 Fuzzy sets and Rough sets
 PAS project
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Overview
 Problems
in CRISs
 Fuzzy sets and Rough sets
 PAS project
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Problems in CRISs
Fuzzy
Term = Term
Rough
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Overview
 Problems
in CRISs
 Fuzzy sets and Rough sets
 PAS project
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Fuzzy sets and rough sets
 Traditional
approach: crisp sets
Young people = {x  People | 0<age(x)<27}
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Fuzzy sets and rough sets
 Fuzzy
Young(x) =
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approach: fuzzy sets
0
if age(x) ≥ 30
1
if age(x) ≤ 20
(30 – age(x)) / 10 otherwise
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Fuzzy sets and rough sets

Rough approach: rough sets

Upper approximation (R↑A)
A = {Numerical Analysis}
R↑A = {Num. Analysis, Ex. Sciences, Statistics, ... , Coding Theory}
B = {Compilers}
R↑B = {Compilers, Programming, GCC, YACC}
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Fuzzy rough sets

Fuzzy approach on rough sets
Fuzzy set A
 Fuzzy relation R
 R (x,y)
 Upper approximation
 (R↑A)(y) = sup min(R(x,y),A(y))

x∈X
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Fuzzy rough sets: application

Query expansion
 Allows
R
Programming
more results by using R↑A
Programming
Hardware
1.0
Hardware
C++
Java
0.8
0.8
1.0
Laptop
Algorithm
0.6
0.4
C++
0.8
1.0
0.7
0.2
Java
0.8
0.7
1.0
0.2
Laptop
Algorithm
0.4
0.6
1.0
0.2
0.2
1.0
- Query: “Programming”
- Expanded query: {(“Programming”,1.0), (“C++”,0.8), (“Java”,0.8),
(“Algorithm”,0.6)}
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Overview
 Problems
in CRISs
 Fuzzy sets and Rough sets
 PAS project
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PAS-project

What is the PAS-project?
Personal Alert System (HoGent)
 Goal: to get the researcher’s attention on funding
possibilities that match his/her profile
 Information: about researchers, projects, funding
possibilities (grants etc.) → matching/collaboration
 Automation and intelligence

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PAS – How does it work?
-Name
Fill in
-Staff number
User
-Department(s)
-Group
-Date of creation of the profile
-Last update of the profile
-Percentage research time
IWETO
-Skills description
Thesaurus
-Diplomas
-Publications
HoGent
-IWETO-keywords
Thesaurus
-Free keywords
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PAS – How does it work?
-Reference
-Title
-Content
-Attachment(s)
-Level
-Duration
Messages
-Institution
-Deadline
IWETO
Thesaurus
-Address
-Contact person
-IWETO-keywords
-Free keywords
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HoGent
Thesaurus
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PAS – How does it work?
1
2
3

The IWETO-classification has 641 research fields:
5 at the 1st level, 31 at the 2nd level, 605 at the 3rd level
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PAS – How does it work?
1
0.6
2
0.7
3
0.8

By adding “free keywords” we can refine the classification
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PAS – How does it work?
Query:
A = {k3}
Expanded query:
R↑A = {(k1,0.8), (k3,1.0), …}
M1 → R2
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PAS – How does it work?
0.6
0.7
0.7
0.8
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PAS – Current implementation




Prototype that will be used as skeleton for the final
system
Basic algorithm using weights and their products and
basic fuzzy rough query expansion1
Basic profiles and messages
Manual processing of feedback and manual data
extraction from text files.
1 P.
Srinivasan, M. E. Ruiz, D. H. Kraft, J. Chen: Vocabulary mining for information
retrieval: rough sets and fuzzy sets, Information Processing and Management, 37(1)
(2001) 15-38
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PAS – Future work







Richer representation of profiles and messages
Automation of the feedback mechanism
Dealing with imprecision and words from different thesauri
Dealing with ambiguity and incomplete profiles
Tracking research activities for collaboration
Automatic extraction of information from text files
Search engine
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Thank you
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