A fuzzy ontology for medical

document retrieval

ACSW Frontiers 2004: Dunedin, New Zealand

報告者 :楊仁杰 學 號 :9454604

outline1. Introduction

2. Fuzzy ontology

3. Membership values in a fuzzy ontology

4. Example of the use of fuzzy ontology

Introduction Searching for information can be an extremely complex task. Many searches performed are inadequate or cursory ex: the techniques of critical reflection

the different uses to which different groups ofmedical professionals use online tools, andhence the different types of resources required are

shown in the work

Introduction These approaches provide large amounts of data, b

ut do not really give any information on the intensions of the searcher, whether their search was successful

One way of overcoming this problem is the postulation of a “fuzzy ontology”.

Fuzzy ontology The fuzzy ontology is based around the

concept that each index term or object is related to every other term (or object) in the ontology , with a degree of membership assigned to that relationship based on fuzzy logic

fuzzy ontology = fuzzy logic + ontology

Fuzzy ontology ontology :

The use,reuse and sharing of information between ontologies, isespecially important in the medical field with the growthof evidence-based medicine(Moody and G. 1999)

Ontology members

“is a” 、 “ uniquely_mapped_to” and “developmental_form_of”.

Fuzzy ontologyfuzzy logic :

The fuzzy membership value μ is used for

the relationship between the term or object

in question where 0<μ<1, and μ

corresponds to a fuzzy membership relation

such as “strongly”,“partially”, “somewhat”,

” slightly”

Fuzzy ontology

A fuzzy ontology scheme.

Fuzzy ontology and collective searching Each user would have their own values

for the membership assigned to terms in the ontology,reflecting their likely information need and world view

Fuzzy ontology and collective searching

This method assigns a membership value for each potential relation for each term

(1)

(2)

Fuzzy ontology and collective searchingEx :

(L1, L2, L3) -> (μ1=0.6, μ2=0.3, μ3=0.1)

L1’s membership value is decreased to 0.5

(L1, L2, L3) -> (μ1=0.5, μ2=0.375, μ3=0.125)

Fuzzy ontology

Membership values in a fuzzy ontology

Fuzzy ontology

5 Parent terms + 19 Child terms

Membership values in a fuzzy ontology

Membership functon for fuzzy ontology

Membership values in a fuzzy ontology

Manual assignment of membership values :

The user can then select these terms according to the degree of relatedness to the original query term. There are boxes for “Opposite”, “Not Related”, “Slightly Related”, “Moderately Related” and “Strongly Related”. A value for “Usefulness” of the document is also recorded.

Membership values in a fuzzy ontologyManual assignment of membership values

A value for “Usefulness” of the document. “Opposite”

A value for “Useless” of the document.

Ex : “Cold” the term “Frigid”

“Opposite”

“Cold” the term “torrid”

Membership values in a fuzzy ontology

Automatic Membership value assignment :

Queries run against both GOOGLE (

www.google.com) and PubMed (pubmed.gov)

each term that exists in multiple locations, is given an equal and proportionate membership value

Membership values in a fuzzy ontology

weighted 3 2 1

PubMed keyword section

Title abstract

Google meta tags Headings main body

local terms (Li)

all terms discovered (Ai)

Example of the use of fuzzy ontologyAND - conjunction

The fuzzy “AND” operator used in this thesis is the min operator.

ex : “ Head” and “Nose” (0.7,0.9)membership

value

a query : “Head AND Nose” ->”Head”

Example of the use of fuzzy ontologyOR - disjunction

The fuzzy “OR” operator used in this thesis is the max operator.

ex : “ Head” and “Nose” (0.7,0.9)membership

value

a query : “Head OR Nose” ->”NOSE”

簡 報 完 畢簡 報 完 畢敬 請 指 導敬 請 指 導