Transcript Ontology-based Knowledge Management System for CREDIT

Ontology Technology and Its
Applications on the Internet
李健興
長榮大學資訊管理系
Outline





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Web Service
Semantic Web
Ontology
Knowledge Management System
Some Applications on the Internet
Conclusion
2
Web Service
The Evolution Of E-business
Web Services
Commerce
Collaboration
Transact
business
Leverage your
experience
Access data
Transform the way you
conduct business
Publish
Get your
information on
the Web
Integrate the Web with
business systems
Security
Chasm
Business
Chasm
V
A
L
U
E
SUN ONE Smart Web Service
What is Web Service?
 A new model for creating dynamic
distributed applications with common
interfaces for efficient communication
across the Internet.
 Self-describing, self-contained,
modular applications that can be mixed
and matched with other Web services
to create innovative products,
processes, and value chains.
7
WWW vs. Web Service
 Web service supports dynamic
interaction
Reader
Human
Machine
Language
HTML
XML
Protocol
HTTP
SOAP
8
The Elements of a Web Service
 Key Players
 Key Functions
 Publish
 Find
 Bound
Service Register
Find
Bind
Service Provider
 The Service Provider
 The Service Requester
 The Service Registry
Publish
Web
Service
Web
Service
Web
Service
Service Requester
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Mobile Web Service for CREDIT Center
Dynamic
Request/
Rule Setting
Personalized
Service
Classification
Service
UDDI
Registry
Workflow
Service
CREDIT KM
System
Intelligent Mobile
Delivery Service
Service
Portal/
Engine
Service
Provider
回應 (SOAP)
搜尋 (UDDI)
註冊 (WSDL)
Service Net
On-line
Tracking
Service
Web Services
Can be
 Described
 Published
 Found
 Bound
 Invoked
 Composed
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Examples of Web Services
 Business information with rich content:
weather reports, credit check, news
feeds, stock quotes, airline schedules,
auctions
 Transactional web services for B2B or
B2C: airline reservations, supply chain
management, rental car agreements,
purchase order.
12
Examples of Web Services
 Business process externalization:
business linkages at the workflow level,
net marketplace, extended supply
chains.
 E-government
 E-learning
 Digital library
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Web Service Mechanism
搜尋Web Service
UDDI
註冊Web Service
取得Web Service資訊
Service
Requester
WSDL
描述Web Service
Service
Provider
實際傳遞需求訊息
SOAP
傳遞回應訊息
UDDI : Universal Description Discovery and Integration
WSDL: Web Service Description Language
SOAP : Simple Object Access Protocol
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SOAP
 Simple Object Access
Protocol
 HTTP + XML
 The most popular protocols
on the internet
 Firewall consideration
 Cross platform messaging
standard
 Is being standardized by
W3C under the name XML
Protocol
SOAP Message
HTTP Header
SOAP Envelope
SOAP Header
SOAP Body
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WSDL
 Web Services Description Language
 Proposed by Ariba, IBM, Microsoft
 WSDL is an XML format for describing
network services
 Binding
 Interface
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UDDI
1.
SydneyNet.com
UDDI Registry
Harbour Metals creates
online website with
local ASP
2.
4.
Consumers and
businesses discover
Harbour Metals and do
business with it
3.
ASP registers
Harbour Metals with UBR
Marketplaces and search engines
query UBR, cache Harbour Metals
data, and bind to its services
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Semantic Web
Background
 Growing complexity in web space
* scale、device types、media type
 Simplicity of HTTP and HTML has caused
bottlenecks that hinder searching,
extracting, maintaining, and generating
information.
 Readable to human  machine
 Knowledgeable usage of webs
 Efficiency in handling web data
understandable.
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Background
 Needs of service automation:
browsing by users to retrieve information 
automatically cooperating by webs to provide services.
So, we need the third generation webs.
(hand written HTML pages
 machine generated HTML pages
 semantic web)
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Layers of Semantic Web
 Unicode + URI (foundation) layer
 XML (syntactic interoperability) layer
 RDF + Schema (data interoperability)
layer
 Ontology (data inter-conversion) layer
 Logic (interoperability) layer
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Architecture of Semantic Web
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RDF and RDF Schema
 Developed by W3C for describing Web
resources, allows the specification of the
semantics of data based on XML in a
standardized, interoperable manner.
 It also provides mechanisms to explicitly
represent services, processes, and
business models, while allowing
recognition of nonexplicit information.
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RDF and RDF Schema
 Basically, RDF is based on O-A-V
representation scheme.
 RDF does not provide mechanisms for
defining the relationships between
properties (attributes) and resources.
 RDFS offers primitives for defining
knowledge models that are closer to
frame-based approaches.
 Protégé, Mozilla, Amaya, etc. adopt
RDF(s).
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Language stack in Semantic
Web
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Ontology
Ontology
 A Revolution for Information Access and
Integration.
 An ontology is a formal, explicit
specification of a shared
conceptualization.
 Conceptualization
 Explicit
 Formal
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Ontology
 The main application areas of ontology
technology
 Knowledge management
 Web commerce
 Electronic business
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What is an Ontology?
 Ontology – explicit formal specifications
of the terms in the domain and
relations among them.
 An ontology contains a hierarchy of
concepts within a domain and
describes each concept’s property
through an attribute-value mechanism.
 Relations between concepts describe
additional logical sentence.
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氣象
Relation
Association
Ontology Example
氣象報導
寒流
颱風
中央氣象局/氣象局
型態：預報人員、
天氣圖
表示、警告、評估
發佈、
表示
提醒
民眾/人民
氣象百科
降雨
......
颱風
編號:***(Neu)號
中心位置：
:***(Nc)(Ncd)(Neu)(
Nf)
強度:輕度颱風
型態:暴風圈
氣壓
型態:副熱帶高氣
壓、熱帶性
低氣壓
增強為、逼近
來襲、形成、登陸
天文
......
降雨
降雨量***(Neu)公釐
累積雨量***(Neu)公
釐
種類:大雨、陣雨、
大雷雨、豪雨
、豪大雨
型態：雨量、打雷
導致、造成
、帶來
發生、襲擊、增加
災害
......
造成
型態:水災、旱象、
土石流、山崩
、洪水、房屋
倒塌、河水暴
漲、落石、雷
擊、霜害
來襲、形成、登陸
發生
向、往
型態:人數
導致
型態:最近、昨日、
今日、白天、
午後
根據、開始
呈現、滯留、徘徊
影響
注意、受困
移動方向
方向:東方、南方
西北方、東
南方
移動、靠近、前進
地區
區域:山區、平地、
台灣、中部、
東半部
各縣市：台北市、台
南縣
海域:東海、南海
海岸:西海岸、沙岸
帶來、引進
氣流
型態:西南氣流、
冷氣流
接近、影響、流動
農林漁牧業
型態:漁港、農田
、農作物、
魚貨量
避風、休耕
時間
DAML+OIL format
<?xml version=‘1.0’ encoding=‘Big5’?>
<rdf:RDF
xmlns:rdf =”http://www.w3.org/1999/02/22-rdf-syntax-ns#”
xmlns:rdfs=”http://www.w3.org/2000/01/rdf-schema#”
xmlns:daml=”http://www.daml.org/2001/03/daml+oil#”
xmlns:xsd =”http://www.w3.org/2000/10/XMLSchema#”
xmlns:a =”http://©.stanford.edu/system#”
>
<daml:Ontology rdf:about=”氣象”>
<daml:imports rdf:resource=”http://www.daml.org/2001/03/daml+oil” />
</daml:Ontology>
<daml:Class rdf:ID=”氣象”>
</daml:Class>
<daml:Class rdf:ID=”氣象報導”>
……
……
……
<daml:range rdf:resource=” #災害”/>
</daml:ObjectProperty>
<daml:ObjectProperty rdf:ID=”影響”>
<daml:domain rdf:resource=” #災害”/>
<daml:range rdf:resource=” #農林漁牧業”/>
</daml:ObjectProperty>
</rdf:RDF>
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Characteristics of Ontology





Formal Semantics
Consensus of terms
Machine readable and processable
Model of real world
Domain specific
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Reasons to Develop Ontologies
 To share common understanding of the
structure of information among people
or software agents.
 To enable reuse of domain knowledge.
 To make domain assumptions explicit.
 To separate domain knowledge from
the operational knowledge.
 To analyze domain knowledge.
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Process of Developing an
Ontology
 Developing an ontology includes:
Determine the domain and scope of the ontology.
Consider reusing existing ontologies.
Enumerate important terms in the ontology.
Define classes in the ontology and arrange the
classes in a taxonomic (subclass-superclass)
hierarchy.
 Define attribute and describe allowed values for
these attribute.
 Fill in the values for attribute for instance.




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Ontology Learning Process
Knowledge Management System
Internet/Intranet
News/Documents
Document
Repository
Enterprise Networking
Resource
Non-structured
Data
XML-based
E-documents
Ontology
Construction
Service
CMMI
Assistant
Service
Meeting
Scheduling
Service
Workflow
Service
Semantic
Search
Service
CMMI-based CREDIT
K.M. System
Automatic
Classification
Service
Ontology
Repository
Document
Abstraction
Service
On-line
Tracking
Service
Intelligent
Mobile Delivery
Service
Personalized
Service
Personal
Ontology
End User
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CREDIT KM System
 Process Management
 Workflow → BPM + Web service
 CMMI (中小企業)
 Mobile Workflow
 Document Management





Knowledge Map (Ontology)
Q and A
FAQ
Personalization
Semantic Search
38
CREDIT KM System
 Meeting Management
 Meeting Scheduling
 Meeting Notification
 Meeting Follow-up
 Message Management
 BBS
 Notification
 Directory Service for Message Delivery
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何謂CMMI

Capability Maturity Model – Integrated (CMMI)
是美國國防部在1991年委託卡內基美隆大學軟
體工程學院所發展出來的一套制度，目的是希
望能提供系統／軟體發展機構持續改善軟體發
展與管理能力
40
Maturity Level 2
Process Area 1（Requirement Management）
Process Area 2（Project Planning）
Process Area 3（Project Monitoring and Control）
Maturity
Level 2
Process Area 4（Supplier Agreement Management）
Process Area 5（Measurement and Analysis）
Process Area 6（Process and Product Quality Assurance）
Process Area 7（Configuration Management）
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Automatic Construction of OO
Ontology
 Use object-oriented data model to
represent ontologies.
 Follow object-oriented analysis
procedure to build ontologies.
 Apply natural language processing
technology to extract key terms from
documents.
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Automatic Construction of OO
Ontology
 Apply SOM clustering technology to find
concepts and instances.
 Apply data mining technology and
morphological analysis to extract
attributes, operations, and associations
of instances.
 Aggregate attributes, operations, and
associations of instances to class.
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Domain Ontology
Domain
Category 1
Category 2
Event E1
Category 3
Event E2
Generalization
……………
Event E3
Category k
Association
……
Event Ep
C1
C2
C3
AC11,AC12 ,…,AC1q1
AC21,AC22 ,…,AC2q2
AC31,AC32 ,…,AC3q3
OC11 ,OC11,…,OC1q1
OC21 ,OC21 ,…,OC2q2
OC31 ,OC31 ,…,OC3q3
C4
C5
Cm
AC41,AC42,…,AC4q4
AC51,AC52,…,AC5q5
OC41,OC41,…,OC4q4
OC51,OC51,…,OC5q5
……
Aggregation
C : Concept
A : Attribute
O : Operation
ACm1,ACm2,…,ACmqm
OCm1 ,OCm1,…,OCmqm
Class-layer
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Concepts Class and Instance
國家
-州(省) : String
-城市 : String
-總統(元首、領導人) : String
-政黨 : String
-單位 : String
-媒體 : String
+改革()
+發展()
美國(美方、美利堅合眾國)
-州(省) : String = 加州、德州
-城市 : String = 紐約、華盛頓、費城、舊金山、芝加哥、洛杉磯
-總統 : String = 布希
-政黨 : String = 共和黨、民主黨
-單位 : String = 白宮、五角大廈、聯邦調查局、太空總署、國務院
-媒體 : String = 華盛頓郵報、CNN
+改革()
+發展()
Domain Ontology Construction
Document
Pre-processing
Special Domain
Documents
Nouns
Chinese
Dictionary
Concept Clustering
Sentences
Episode Extraction
Concepts
Attributes, Operations,
Associations Extraction
Episodes
DAML+OIL /
OWL Format
Domain
Ontology
Data Flow
Control Flow
Common
Data Flow
Ontology Construction Agent
Input
Documents
Part-Of-Speech
Tagger
Stop Word
Filter
Concept
Extractor
Domain Term
Combination
Processer
Episode
Extractor
Episode Net
Extractor
Chinese
English
Term
Term
Dictionary Dictionary
HowNet
WordNet
Knowledge Base
Genetic
Learning
AttributesOperationAssociation
Extractor
Nouns/
Verbs
Repository
Chinese
Data Flow
Concepts
Repository
English
Data Flow
Episodes
Repository
Episode Net
Repository
…
…
Chinese Domain English Domain
47
Ontology
Ontology
Episodes Extractor

An episode is a partially ordered
collection of events occurring together.
48
Episodes Extractor

The following shows an example of
extraction of episode from a sentence
德國門將卡恩贏得本屆世足賽代表最佳球員的金球獎。
POS Tagger
德國(Nc) 門將(Na) 卡恩(Nb) 贏得(VJ) 本(Nes) 屆(Nf) 世足賽(Nb) 代表
(Na) 最佳(A) 球員(Na) 的(DE) 金球獎(Nb)。(PERIODCATEGORY)
Stop Word Filter
(德國, Nc, 1) (門將, Na, 2) (卡恩, Nb, 3) (贏得, VJ, 4)
賽, Nb, 5) (代表, Na, 6) (球員, Na, 7) (金球獎, Nb, 8)
(世足
Episode Extractor
德國(Nc)_門將(Na)_卡恩(Nb)
Germany_keeper_Oliver Kahn
卡恩(Nb)_贏得(VJ)_金球獎(Nb)
Oliver Kahn_took_Golden Ball
49
Document Abstraction Agent
OFEE Agent
Internet
e-News
Retrieval
Agent
Document
Processing Agent
POS Tagger
(CKIP)
Fuzzy Inference
Agent
Chinese Term
Filter
PDA
Real-time
e-News
Repository
…
Chinese e-News
Ontology
Event Ontology
Filter
Chinese
e-News
Summary
Repository
Summarization Agent
Cell Phone
G
U
I
Notebook
Extracted-Event
Ontology
e-News Repository
Sentence
Rule Base
Sentence
Generation
Agent
Chinese
e-News
Summar
y
50
Semantic Search
 Human-readable
 HTML
 Machine-readable
 XML
 Machine-understandable
 Semantic Web with Ontology (RDF,DAML+OIL,
OWL)
51
Semantic Search
 Keyword-based search
 Single-word query
 Context query
 Boolean query
 Conceptual search
 Conceptual query
 Natural language query
 Semantic search
 Ontology-reasoning query
52
Why Semantic Search
 Mass information make user confused,
current search engines are not good
enough. (e.g. 腦科 v.s. 電腦科學)
 Quality is more important than
Quantity
 Search by "what they means" not just
"what they say"
 The user who has no idea about
domain terminologies can’t find
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information easily.
Semantic Search Architecture
Query processing
Document Preprocessing
End User
WWW
Repository
Query
Information
Retrieval
Agent
Natural
Language
Processing
Indexing and
Gathering statistics
Parsing and
Transforming formats
CKIP
Repository
Ontology
Repository
Query Inference
Personal
Thesaurus
Repository
Query
Personalization
Index
Repository
XML file
Repository
Clustering
Query Results
Question & Answer System
 Question analysis
 5W1H
 what, who, when, where, why, and how.
 Indirectly question & other
 YesNo question…etc.
 Answer analysis
 Question type
 5W1H
 Domain
 Domain knowledge
55
Question & Answer System
Knowledge Extraction Subsystem
Knowledge extraction & learning process
Documents
Question & Answer Knowledge Base
Ontology
supervision
Domain
Ontology
KM
Question
Ontology
Answer
Ontology
KM
Question
Ontology
Search workflow Q&A
engine
User query process
Question Answering Subsystem
workflow Q&A PM
What How Where
Answering process
Receive
User query
Return
Answer
User
Question & Answer Knowledge
Base
 Domain ontology
 Object-oriented ontology
 Question ontology
 The knowledge of question domain
 To Classify and extract question
 Answer ontology
 The knowledge map of Q&A knowledge base
57
Question & Answer Knowledge
Base
 Alternation Rule
 Morphological
 Lexical
 Semantic
 Ontology supervision
 Ontology management
 Ontology inference
58
Ontology Based Personalized
Information Service
 Make a specific information service that
can adapt to the behavior of each user.
 Provide a mechanism that can observe
and analyze the browsing behavior of
each user.
 Produce a structure with personal
custom and preferences for other
services using.
59
Personal Ontology
User Behavior & Browsed Content Analysis
Log File
Recording
Engine
End User
Personal
Log Files
Repository
<<History>>
WWW
Present Browsing
Behavior
Sequence of User Browsing Behavior
s1
s2
s3
…
sn
User Behavior & Browsing Content Analysis
…
Web Content
Recording
Engine
User Used
Behavior
Functionalization
Browsed
Contents
Repository
<<Present>>
Domain
Ontology
Content
Conceptualization
&
Weighting
<<Present>>
Function of
Browsing
Frequency
Preference
Degree
Concepts and Weights
of Browsed
Content
Function of
Browsing
Time
Fuzzy
Inference
Domain
Ontology
Personal
Ontology
Additional Weighting of
Related Sequence Concepts
User Behavior Analysis
 In order to find out user’s favor
tendency, the first job is analyzing the
habitual behavior of reading.
 Consider two features: reading time and
reading frequency.
 Consider reading time is related with
content length, change the feature to
time
log length
61
Personal Ontology
Personal
Log Files
Repository
Browsed
Contents
Repository
Browsing
Frequency
Browsing
Time
Function of
Browsing
Frequency
Frequency
Feature Data
Feature Data
Processing
Content
Length
Feature Data
Functionalization
Browsing Time
Feature Data
Function of
Browsing
Time
Feature Data Functionalization
Feature
Data
Data Sorting
Sorted
Data
Data Clustering
Function
of Feature
Data
Functionalization
…
Meeting Scheduling Architecture
Fuzzy Inference Agent
attend possibility 1
y1
attend possibility 2
y2
attend possibility n
yn
Layer 5
(output
linguistic
nodes)
…
Layer 4
(output
term
nodes)
.....
Layer 3
(rule
nodes)
Layer 2
(input
term
nodes)
Layer 1
(input
linguistic
nodes)
…
x11
x12
invitee 1
…
x1m
x21
x22
invitee 2
…
…
x2m
xn1
xn2
invitee n
xnm
64
Genetic Learning Agent
Start
Personalized
Knowledge
Base (PKB)
Initiation
Meeting
Information
Knowledge
Base (MIKB)
Current
Population
elitism
Selection
Crossover
Evaluation
Mutation
Fuzzy Inference
Agent (FIA)
replace
New
Population
65
Some Applications on the Internet
A Fuzzy Ontology and Its Application
to News Summarization
C-S Lee, Z-W Jian, and L-K Huang, (SCI) IEEE Transactions on Systems, Man and
Cybernetics Part B, 2005. (To be published)
Fuzzy Ontology
Domain
Category 1
Category 2
Category 3
Generalization
……………
Category k
Aggregation
Association
Event E1
Event E2
Event E3
……
Event Ep
{C1;μC1E1,μC1E2,…,μC1Ep}
{C2;μC2E1,μC2E2,…,μC2Ep}
{C3;μC3E1,μC3E2,…,μC3Ep}
AC11,AC12 ,…,AC1q1
AC21,AC22 ,…,AC2q2
AC31,AC32 ,…,AC3q3
OC11 ,OC11,…,OC1q1
OC21 ,OC21 ,…,OC2q2
OC31 ,OC31 ,…,OC3q3
{C4;μC4E1,μC4E2,…,μC4Ep}
{C5;μC5E1,μC5E2,…,μC5Ep}
AC41,AC42,…,AC4q4
AC51,AC52,…,AC5q5
OC41,OC41,…,OC4q4
OC51,OC51,…,OC5q5
LBR
LNR
C : Concept
A : Attribute
O : Operation
{Cm;μCmE1,μCmE2,…,μCmEp}
……
ACm1,ACm2,…,ACmqm
OCm1 ,OCm1,…,OCmqm
Class-layer
68
Fuzzy Ontology Construction
…
Interne
t
Domain
Expert
Chinese
News
Dictionary
Retrieval
Agent
News
Corpus
Document
Preprocessing
Mechanism
Domain Ontology
Concept Set
Meaningful
Terms
Term
Classifier
Classified
Meaningful
Term Set
Fuzzy
Inference
Mechanism
…
Fuzzy Ontology
69
Fuzzy Inference Mechanism
{C1 ;  C1E1 ,  C1E2 ,,  C1EP }
…
{Cm ;  Cm E1 ,  Cm E2 ,,  Cm EP }
I1
…
Im
Layer 7
(Integration
layer)
Layer 6
(Summation
layer)
ΣC1E1
yE111
Layer 5
(Output
linguistic
layer)
Layer 4
(Output
term
layer)
Layer 1
(Input
linguistic
layer)
…
…
TRS
TRS
yE1n11 …
yE11m
COA
…
…
μC1EP
ΣCmE1
…
COA
ΣC1Ep
yE1n1m
…
COA
TRS
TRS
COA
μCmEP
yEp11
TRS
TRS
…
POS
TE11
TW
. . TE1i
SD
TRS
…
. . TE n
1 1
POS
..
TW
Cj
Cm
Concept set of domain ontology
Event 1
TRS
COA
…
COA
TRS
TRS
TRS
…
…
POS
SD
..
TRS
TRS
…
C1
Term set of event 1
yEpnp1 …
… yEp1m
…
yEpnpm
…
COA
TRS
TRS
COA
…
TRS
…
ΣCmEp
…
…
TRS
Layer 3
(Rule
layer)
Layer 2
(Input
term
layer)
…
μCmE1
μC1E1
TEp1
TW
. . TEpi
SD
TRS
…
…
. . TE n
p p
TRS
TRS
…
POS
C1
..
TW
Cj
SD
..
Cm
70ontology
Concept set of domain
Term set of event p
Event p
News Agent for News Summarization
Document
Preprocessing
Mechanism
…
Fuzzy
Ontology
Sentence Path
Extractor
Retrieval
Agent
Sentence
Generator
News Agent
Sentence
Filter
Interne
t
News
Summary
Repository
71
Sentence Path Extractor
{中央氣象局; 0.99, 0.85, 1.0}
{Central Weather Bureau; 0.99,
0.85, 1.0}
A中央氣象局:{Null}
ACWB:{Null}
O中央氣象局:{Null}
OCWB:{Null}
研判
Expect
{未來時間; 0.79, 0.66, 0.84}
{Future Time; 0.79, 0.66, 0.84}
A未來時間:{明天}
AFT:{Tomorrow}
O未來時間:{Null}
OFT:{Null}
有
{降雨; 1.0, 0.96, 1.0}
有
Has
{颱風; 1.0, 0, 1.0}
{Rain; 1.0, 0.96, 1.0}
Has
{Typhoon; 1.0, 0, 1.0}
A降雨:{降雨}
A颱風:{中度颱風}
AR:{Rain}
AT:{A medium typhoon}
O降雨:{發生}
O颱風:{Null}
OR:{Occur}
OT:{Null}
侵襲
{台灣地區; 1.0, 0.99, 1.0}
Attack
{Taiwan; 1.0, 0.99, 1.0}
A台灣地區:{台灣}
AAT:{Taiwan}
O台灣地區:{Null}
OAT:{Null}
72
Sentence Path Extractor (cont.)
 The Sentence Path Extractor uses the Depth-FirstSearch algorithm to look for possible sentence
paths from the fuzzy ontology.
Sentence Path P: [Concept Name]→[Concept Name]→…→[Concept Name]
Sentence Path 1:
“[中央氣象局]→[未來時間]→[颱風]→[台灣地區]”
Corresponding English Version:
“[Central weather bureau]→[Future time]→[Typhoon]→[Taiwan]”
Sentence Path 2:
“[中央氣象局]→[未來時間]→[降雨]”
Corresponding English Version:
“[Central weather bureau]→[Future time]→[Rain]”.
73
Sentence Generator
 The Sentence Generator generates a set of
sentences based on the temporary fuzzy ontology.
Sentence Si: [Concept Name, Attribute_value, Operation]→Relation→[Concept Name, Attribute_value,
Operation]→Relation→…→[Concept Name, Attribute_value, Operation] (Possibility wi)
Sentence 1:
“[中央氣象局, Null, Null]→研判→[未來時間, 明天, Null]→有→[颱風, 中度颱風, Null]→侵襲→[台灣
地區, 台灣, Null] (possibility 0.78)”
Corresponding English Version:
“[Central weather bureau, Null, Null]→Expect→[Future time, Tomorrow, Null]→Has→[Typhoon, A
medium typhoon, Null]→Attack→[Taiwan, Taiwan, Null] (Possibility 0.78)”
Sentence 2:
“[中央氣象局, Null, Null]→研判→[未來時間, 明天, Null]→有→[降雨, Null, 發生] (Possibility 0.84)”
Corresponding English Version:
“[Central weather bureau, Null, Null]→Expect→[Future time, Tomorrow, Null]→Will→[Rain, Null, Occur]
(Possibility 0.84)”
74
Sentence Filter
 The Sentence Filter is used to filter the redundant
sentences.
Sentence:
S1: “[中央氣象局, Null, Null]→研判→[未來時間, 明天, Null]→有→[颱風, Null, Null]→侵襲→[台灣地
區, Null, Null] (Possibility 0.78)”
S2: “[颱風, Null, Null]→侵襲→[台灣地區, Null, Null] (Possibility 1.0)”
Corresponding English Version:
S1: “[Central weather bureau, Null, Null]→Expect→[Future time, Tomorrow, Null]→Has→[Typhoon, Null,
Null]→Attack→[Taiwan, Null, Null] (Possibility 0.78)”
S2: “[Typhoon, Null, Null]→Attack→[Taiwan, Null, Null] (Possibility 1.0)”
Result of the semantic sentence:
SS: 中央氣象局研判明天有颱風侵襲台灣地區 (Possibility 0.78)
Corresponding English Version:
SS: The central weather bureau expects that the typhoon will attack Taiwan tomorrow. (Possibility 0.78)
75
Experimental Results
Expert 1
Expert 2
Expert 3
Average Precision
1
Expert 1
Expert 2
Expert 3
Average Precision
1
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
0
5
10
The number of news
15
(a) The results of 2002 typhoon news
0
0
20
Expert 1
Expert 2
Expert 3
Average Precision
5
10
The number of news
15
20
(b) The results of 2003 typhoon news
0
5
10
The number of news
15
20
(c) The results of 2004 typhoon news
The average precision of typhoon event evaluated by three domain experts.
Expert 1
Expert 2
Expert 3
Average Recall
1
Expert 1
Expert 2
Expert 3
Average Recall
1
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
0
5
10
The number of news
15
(a) The results of 2002 typhoon news
20
Expert 1
Expert 2
Expert 3
Average Recall
0
0
5
10
The number of news
15
(b) The results of 2003 typhoon news
20
0
5
10
The number of news
15
20
(c) The results of 2004 typhoon news
The average recall of typhoon event evaluated by three domain experts.
76
Experimental Results (cont.)
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
0
0
5
10
The number of news
15
20
Fuzzy Ontology
OFEE
Fuzzy Ontology Average Precision
OFEE
1
Fuzzy Ontology Average Precision
OFEE
1
Average Precision
0
(a) The results of 2002 typhoon news
5
10
The number of news
15
0
20
(b) The results of 2003 typhoon news
5
10
The number of news
15
20
c) The results of 2004 typhoon news
The average average curves of Fuzzy Ontology-based agent and OFEE agent for typhoon event.
Fuzzy Ontology
OFEE
Average Recall
1
Fuzzy Ontology Average Recall
OFEE
1
Average Recall
1
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0
0
0
5
10
The number of news
15
(a) The results of 2002 typhoon news
20
Fuzzy Ontology
OFEE
0
0
5
10
The number of news
15
(b) The results of 2003 typhoon news
20
0
5
10
The number of news
15
20
(c) The results of 2004 typhoon news
The average recall curves of Fuzzy Ontology-based agent and OFEE agent for typhoon event.
77
Experimental Results (cont.)
<?xml version="1.0" encoding="Big5"?>
<News>
<Title>西南氣流影響南部有雷陣雨查特安颱風增強</Title>
<Date>2002/07/05</Date>
<Content>
雖然雷馬遜颱風已遠離台灣，不過，受西南氣流影響，中央氣象局預測，今天南部會有雷陣雨，中部以北及東北部午
後也可能有短暫陣雨，其他地區是多雲到晴的天氣，另外，關島附近的六號颱風查特安今天上午增強為中度颱風。氣
象局說，雷馬遜颱風已進入黃海，正朝韓國南部而去，今天台灣北部海面風力也逐漸減弱，不過，上午平均風力仍有
七級到八級，最大陣風有十級，台灣附近各海面平均風力也有六級到八級，最大陣風十級。今天各地氣溫仍偏高，氣
象局預測北部、南部都有攝氏三十五度以上，中部約三十四度。另在關島附近的第六號颱風查特安今天上午八時增強
為中度颱風，氣象局說，查特安目前的位置大約在鵝鑾鼻東南東方約二千七百公里海面上，以每小時二十一公里速度
朝西北方向行進，七級風暴風半徑二百公里，目前因離台灣很遠，還要再觀察幾天才能確定台灣是否會受到影響。
</Content>
</News>
Date: 2002/07/05
Event Title: 颱風
Summarization Sentences:
颱風遠離台灣地區 (Possibility 1.0)
台灣地區有降雨 (Possibility 0.99)
中央氣象局預測今天有颱風 (Possibility 0.76)
Compression rate: 9%
78
Experimental Results (cont.)
<?xml version="1.0" encoding="Big5"?>
<News>
<Title>鋒面接近 明天盼好雨</Title>
<Date>2002/05/13</Date>
<Content>
明(14)日梅雨鋒面接近，台灣中部以北、東北部、東部地區及馬祖、金門、澎湖將轉為有陣雨或雷雨的天氣，可望帶
來明顯雨勢。今(13)日則除台灣東北部地區有短暫陣雨，山區午後有局部陣雨外，台灣其他各地、澎湖、金門及馬祖
為多雲到晴的天氣，全台白天最高溫將達32、33度左右。
</Content>
</News>
Date: 2002/05/13
Event Title: 降雨
Summarization Sentences:
台灣地區有降雨 (Possibility 1.0)
鋒面接近台灣地區 (Possibility 0.99)
Compression rate: 12%
79
A Genetic Fuzzy Agent Using Ontology
Model for Meeting Scheduling System
C-S Lee, C-C Jiang and W-M Chang, (SCI) Information Sciences, 2005. (Accepted)
Ontology-based Meeting Scheduling
Agent
attend possibility 1
y1
Layer 5
(output linguistic
nodes)
attend possibility n
yn
…
BL: Before Learning
Layer 4
(output term
nodes)
AL: After Learning
.....
Layer 3
(rule nodes)
Layer 2
(input term
nodes)
Layer 1
(input linguistic
nodes)
…
…
x11
invitee 1
…
xn1
x1m
Fuzzy Personal Ontology 1
Meeting Activity
Course Activity
E1:University Meeting
...
Fuzzy Personal Ontology n
Free Time Activity
E2 :Department Meeting
...
Meeting Subject Preference
Attend Meeting Possibility
Linguistic Terms (BL):
Linguistic Terms (AL):
Meeting Detail:
Expectancy Value:
Practical Value:
User Priority
Linguistic Terms(BL):
Linguistic Terms(AL):
Groves of academe:
Investitive:
Linguistic Terms(BL):
Linguistic Terms(AL):
Form:
Linguistic Terms(BL):
Linguistic Terms(AL):
Groves of academe:
Form:
Meeting Activity
Course Activity
E1:University Meeting
E3:Research Meeting
Time
Date:
Duration:
Week:
Time Slot:
Length:
Place
Office:
Conference Room:
Laboratory:
Assembly Hall:
Free Time Activity
...
Meeting Subject Preference
Attend Meeting Possibility
Linguistic Terms(BL):
Linguistic Terms(AL):
Kilometers:
Linguistic Terms (BL):
Linguistic Terms (AL):
Hours:
...
E2 :Department Meeting
Meeting Place Preference
Meeting Time Length
Meeting Event Priority
xnm
invitee n
...
Linguistic Terms(BL):
Linguistic Terms(AL):
Meeting Detail:
Expectancy Value:
Practical Value:
User Priority
Linguistic Terms(BL):
Linguistic Terms(AL):
Form:
E3:Research Meeting
Meeting Place Preference
Linguistic Terms(BL):
Linguistic Terms(AL):
Kilometers:
Meeting Time Length
Meeting Event Priority
Linguistic Terms(BL):
Linguistic Terms(AL):
Hours:
Linguistic Terms(BL):
Time
Linguistic Terms(BL):
Linguistic Terms(AL):
Linguistic Terms(AL): Date:
Groves of academe:
Duration:
Groves of academe:
Investitive:
Week:
Form:
Time Slot:
Length:
Place
Office:
Conference Room:
Laboratory:
Assembly Hall:
81
Meeting Scheduling Ontology
Meeting Scheduling Ontology
Teacher
E1:University
Meeting
Who
What
Teacher
Title :
Dept.:
Teaching subject:
E-mail :
Phone :
Meeting Activity
Type:
Date:
Place:
Event Importance:
People Importance:
Research Assistant
E2:Department
Meeting
……
……
Research Assistant
Source :
Title:
Participate project:
E-mail :
Phone :
Student
E3:Research
Meeting
Postgraduate
Degree:
Dept. : Course :
E-mail : String
Phone: String
Course Activity
Name:
Teacher:
Student:
Time:
Place:
Account number :
Generalization
Aggregation
Association
Undergraduate
Team :
Class :
Course :
E-mail :
Phone :
Free Time Activity
Type:
Time:
Place:
Record Attendance
Meeting detail:
Expectancy Value:
Practical Value:
User’s Preference for Meeting
How
Type :
Place:
Time slot :
Attendee:
When
Time
Date:
Duration:
Week:
Time slot:
Length:
Where
Place
Office:
Conference Room:
Lab.:
Assembly hall :
82
Personal Ontology
Generalization
Personal Ontology
Aggregation
Association
Meeting Activity
……
Course Activity
E1
:University
Meeting
E2
User priority
E3
:Department
Meeting
……
:Research
Meeting
Meeting Subject Preference
Meeting Event Priority
Type:
Groves of academe:
Investitive:
Type:
Groves of academe:
Form:
User’s Preference for Meeting
Attend Meeting Possibility
GFA Record
Type :
Place:
Time slot :
Attendee:
Meeting detail:
Expectancy Value:
Practical Value:
Place
Office:
Conference Room:
Lab.:
Assembly hall :
Time
Date:
Duration:
Week:
Time slot:
Length:
Type:
Form:
Meeting Place Preference
Meeting Time Length
Type:
Kilometers:
Type:
Hours:
Free Time Activity
Type:
Time:
Place:
83
Genetic-based Fuzzy Image Filter and
Its Application to Image Processing
C-S Lee, S-M Guo, and C-Y Hsu, (SCI) IEEE Transactions on Systems, Man and
Cybernetics Part B, 2005. (To be published)
Genetic-based Fuzzy Image Filter
Sample
Images
Fuzzy Number
Construction process
Image
Knowledge
Base
MAE(Y, S) < threshold
or
N generations evolved
S
(Noise-free Image)
X
(Noise Image)
Genetic Learning Process
Fuzzy Filtering Process
Y
85
A 8-neighborhood
x1
x2
x3
x4
x5
x6
x7
x8
x9
86
Fuzzy Filtering Process
y
f+(·)
μ1
μ3
3
Parallel Fuzzy Inference Mechanism
Layer 3
μ5
3
…
RVDK
3
RMD
…
RVBR
fcomp(·)
f×1(·)
f×2(·)
flarge
fsmall
f-(·)
Fuzzy Decision Process
Layer 2
… …
…
… …
… …
…
… …
fF_mean
Layer 1
…
…
Input Variables
…
x
…
1
…
x
4
x
5
…
Fuzzy Mean Process
…
…
x
9
87
Genetic Learning Process
Begin
Initiation
Image
Knowledge
Base
replace
Current
Population
elitism
Selection
S
(Noise-free Image)
Evaluation Module
Output
Image
Crossover
Y
Fuzzy Filtering Process
Mutation
New
Population
88
Experimental Results
(a)
(b)
(c)
(a) Original “Lena” image, (b) noisy image corrupted by salt-and-pepper impulse noise (corruption rate 0.4)
and (c) result yielded by genetic learning after 50 generations.
89
Experimental Results (cont.)
membership
degree
VDK
DK
MD
BR
membership
degree
VBR
VDK
DK
MD
BR
VBR
1
1
0
255
gray level
0
255
gray level
(a)
(b)
(a) The fuzzy sets of “Lena” image constructed by fuzzy number construction process, (b) The tuned fuzzy
sets by fuzzy filtering process and genetic learning process.
90
Experimental Results (cont.)
After tuning
Before tuning
δ
[a, b, c, d]
Fuzzy Rules
δ
[a, b, c, d]
[0, 0, 25, 55]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
1
[0, 40, 45, 67]
[1, 1, 0, 1, 1, 1, 1, 1, 1]
1
[25, 55, 85, 115]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
1
[45, 69, 85, 114]
[0, 1, 1, 1, 0, 1, 1, 1, 0]
0.5
MD
[85, 115, 145, 175]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
1
[110, 115, 120, 166]
[1, 1, 1, 1, 0, 1, 1, 1, 0]
0.5
BR
[145, 175, 205, 235]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
1
[145, 178, 185, 233]
[1, 1, 1, 1, 0, 1, 0, 0, 0]
0.5
VBR
[205, 235, 255, 255]
[1, 1, 1, 1, 1, 1, 1, 1, 1]
1
[215, 241, 241, 255]
[1, 0, 1, 1, 0, 0, 0, 0, 0]
2
VDK
DK
Fuzzy Rules
l
72
72
α
25
64
β
235
207
The parameters of fuzzy sets constructed by GFIF for additive middle-tailed impulse noisy “Lena” image
with a corruption rate 0.4.
91
Experimental Results (cont.)
Fitness
Fitness
2.5
1
GFIF(0.9/0.1)
GFIF(0.9/0.05)
GFIF(0.6/0.1)
GFIF(0.6/0.05)
GFIF(0.9/0.1)
GFIF(0.9/0.05)
GFIF(0.6/0.1)
GFIF(0.6/0.05)
2.25
2
0.75
1.75
1.5
1.25
0.5
1
0
4
8
12
16
20
24
28
Generations
(a)
32
36
40
44
48
0
4
8
12
16
20
24
28
32
36
40
44
48
Generations
(b)
Values of fitness obtained during the learning process and effects of different choices of genetic parameters
for (a) “Albert” and (b) “Cameraman”.
92
Experimental Results (cont.)
(a) Noise corruption rate 0.2.
(b) Result of GFIF before
tuning. (fuzzy inference
alone)
(c) Result of GFIF after
tuning. (fuzzy inference with
genetic learning)
(d) Noise probability 0.8
(e) Result of GFIF before
tuning. (fuzzy inference
alone)
(f) Result of GFIF after
tuning. (fuzzy inference with
genetic learning)
Results of salt-and-pepper noisy image “Albert” with noisy corruption rate p, where p=0.2 and 0.8.
93
Experimental Results (cont.)
Filters
p=0.1
p=0.2
p=0.3
p=0.4
p=0.5
p=0.6
p=0.7
Filters
p=0.1
p=0.2
p=0.3
p=0.4
p=0.5
p=0.6
p=0.7
CWM
33.67
25.58
20.14
16.02
12.94
10.73
8.97
CWM
27.55
23.39
19.01
15.41
12.66
10.47
8.94
SDROM
34.19
33.37
32.46
29.86
26.65
22.34
18.72
SDROM
31.27
30.57
29.43
27.68
24.90
21.65
18.21
RUSSO
45.93
41.96
37.69
33.59
30.03
25.79
22.25
RUSSO
39.23
35.85
32.92
30.47
27.34
24.25
20.91
NASM
37.64
35.58
33.52
31.02
28.97
26.17
23.82
NASM
28.44
27.11
26.24
24.81
23.17
21.75
20.23
FIF
29.38
29.22
28.82
28.49
27.89
27.48
26.91
FIF
26.96
26.81
26.50
26.30
25.87
25.46
24.95
FNN
35.35
29.22
25.95
23.32
21.03
19.08
16.71
FNN
35.83
28.66
25.10
22.57
20.26
18.36
16.13
GFIF
32.43
32.12
31.49
30.84
30.09
29.28
28.32
GFIF
29.80
29.39
28.82
28.21
27.20
26.32
25.54
PSNR values of the compared approaches
for additive long-tailed impulse noisy “Lena”
image with the corruption rate p, where p
=0.1 to 0.7.
PSNR values of the compared approaches
for additive long-tailed impulse noisy
“Bridge” image with the corruption rate p,
where p =0.1 to 0.7.
94
Experimental Results (cont.)
(a) Original image
(256256 pixels)
(e) SD-ROM result
(b) Original image (5050
pixels)
(f) FIF result
(c) Corruption rate 0.6
(g) FNN result
(h) HFF result
(d) Russo result
(i) GFIF result
Results of salt-and-pepper noisy “Boats” image by salt-and-pepper noise with corruption rate p, where p=0.6.
95
Experimental Results (cont.)
(a) Original image
(256256 pixels)
(e) SD-ROM result
(b) Original image (5050
pixels)
(f) FIF result
(c) Corruption rate 0.6
(g) FNN result
(h) HFF result
(d) Russo result
(i) GFIF result
Results of additive long-tailed impulse noisy “Baboon” image by additive long-tailed impulse noise with
corruption rate p, where p=0.6.
96
CMMI Assistant Tools
Architecture of CMMI Assistant with SIM
Project Management services
1.4.3.2
Engineering services
REQM services
1.4.2.1
1.4.3.1
1.4.2.2
Project Closure
services
PP services
1.4.3.6
Testing services
RD services
1.4.3.4
1.4.3.3
PMC services
1.4.2.3
1.4.4.1
MA services
1.4.4.2
CM services
Supporting services
MDA supporting
services
1.4.1.2
Organizational Process
Editor services
1.4.1.3
Gap Analysis
Supporting
services
Pre Assessment Support
services
Process Management services
1.3.0
Service Delivery
Global
Working Space
OCL supporting
services
1.4.1.1
1.4.4.3
PPQA
services
UML supporting
services
1.4.3.5
F
D
Sub Project 1, 2 and 4
98
Architecture of RD Service
Project Member
Project Manager
RD User Interface 1.4.3.1.1
Customer Requirements
Service 1.4.3.1.2 (SP1.1,
SP1.2)
Analyze / Validate
Requirements Service
1.4.3.1.4 (SP3.1, SP3.2,
SP3.3, SP3.4, SP3.5)
Product Requirements
Service 1.4.3.1.3 (SP2.1,
SP2.2, SP2.3)
Requirements
Development Database
1.4.3.1.5
Requirements
Management Service
1.4.3.2
Testing Service
1.4.3.6
Configuration
Management Service
1.4.4.3
99
Architecture of REQM Service
Project Member
Project Manager
REQM User Interface 1.4.3.2.1
Requirements Commitment
Record Service 1.4.3.2.2 (SP1.1,
1.2, 1.5)
Project Planning Service
1.4.2.2
Requirements Change
Management Service 1.4.3.2.3
(SP1.3)
Requirements Tracking Service
1.4.3.2.4 (SP1.4)
Requirements Management
Database 1.4.3.2.5
Requirements Traceability
Ontology 1.4.3.2.6
Configuration Management
Service 1.4.4.3
Project Monitoring and
Control Service 1.4.2.3
Requirements Development
Service 1.4.3.1
Domain Expert
100
Future Work
異質醫護資訊模型與擷取技術
異質醫護資訊模型與擷取技術-研究情境
Data Web
照護小組
RHC, ICU, RCC,RCW
病歷資料、病況記錄
SWG
救護網
個案資料
呼吸器廠商
呼吸器警示、Log記錄
DBMS
<XML>
Documents
Equipment/
Device signal
SWG
SWG
SWG
Data 1
Data 2
Data 3
Data Collector
Personal Healthcare Ontology
Healthcare
Ontology
Query
GUI
Expert
Service Grid Server
SWG:Service Web Gateway
Inference Engine
Rule Base
Repository
Expert
102
預期研究方法
 支援分散式Healthcare data object namespace之分析對應
 以Ontology建構照護領域資訊模型
 利用人工智慧、自然語言處理及機器學習方式擷取Domain
Knowledge。
 Data Collector
 利用自然語言處理及資訊擷取技術以建置、維護個人之
Personal Healthcare Ontology。
 Domain Rule DB
 利用相關推論機制及資料探勘技術進行Knowledge Inference
Engine之研發。
 Query GUI
 透過查詢維護之雛形介面作驗證以串接各元件之整合應用情境
103
預期研究方法 (cont.)
 Distributed Healthcare
Information Model and
Extraction Technology
 Data Collector
 Inference Engine
 Query GUI
DBMS
Metadata
<XML>
Document
Data
Collector
 Personal Healthcare
Ontology based on metadata
Query
repository predefined by
GUI
domain expert
 Rule Base predefined by 3. Report Information
domain expert
Equipment/
Device Signal
1. Distributed Data
Collection
Personal Healthcare Ontology
Inference
Engine
Rule
Base
2. Rule Inference
104
Conclusion
 Web service will be the common
platform of e-life.
 Semantic web makes web services
more autonomous, understandable,
collaborative and intelligent.
 Knowledge management makes
higher-level information/knowledge
usage.
 Ontology is important for the Internet
Application
105
Thank You for Your Attention