05/06/2007 CRIS 2008

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

RoughTerm = Term

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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 approach: fuzzy sets

0 if age(x) ≥ 301 if age(x) ≤ 20(30 – age(x)) / 10 otherwise

Young(x) =

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Fuzzy sets and rough sets Rough approach: rough sets

Upper approximation (R↑A)

A = {Numerical Analysis} B = {Compilers}

R↑A = {Num. Analysis, Ex. Sciences, Statistics, ... , Coding Theory} 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) = min(R(x,y),A(y))Xx∈

sup

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Fuzzy rough sets: application

Query expansionAllows more results by using R↑A

R Programming Hardware C++ Java Laptop Algorithm

Programming 1.0 0.8 0.8 0.6

Hardware 1.0 0.4

C++ 0.8 1.0 0.7 0.2

Java 0.8 0.7 1.0 0.2

Laptop 0.4 1.0

Algorithm 0.6 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

-Staff number

-Department(s)

-Group

-Date of creation of the profile

-Last update of the profile

-Percentage research time

-Skills description

-Diplomas

-Publications

-IWETO-keywords

-Free keywords

Fill in

IWETO

Thesaurus

HoGent

Thesaurus

User

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PAS – How does it work?

-Reference

-Title

-Content

-Attachment(s)

-Level

-Duration

-Institution

-Deadline

-Address

-Contact person

-IWETO-keywords

-Free keywords

IWETO

Thesaurus

Messages

HoGent

Thesaurus

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PAS – How does it work?

The IWETO-classification has 641 research fields:

5 at the 1st level, 31 at the 2nd level, 605 at the 3rd level

1

2

3

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PAS – How does it work?

By adding “free keywords” we can refine the classification

1

2

3

0.6

0.7

0.8

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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

0.80.7

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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