Ontology-based Knowledge Management System for CREDIT

Research Center

長榮大學資訊管理系李健興 博士

2

Outline

CREDIT Research Center Web Service Semantic Web Ontology Knowledge Management System Conclusion

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CREDIT Research Center

Located at National Cheng Kung University. Supported by Walsin Lihwa Group. Contain three main research groups. More than 10 professors and 50 Ph.D or ma

ster students.

Web Service

The Evolution Of E-business

AccessAccess datadata

CommerceCommerce

TransactTransactbusinessbusiness

Leverage yourLeverage yourexperienceexperience

WebWeb ServicesServices

PublishPublish

CollaborationCollaboration

SecuritySecurityChasmChasm

BusinessBusinessChasmChasm

IntegrateIntegrate the Web with the Web with business systemsbusiness systems

TransformTransform the way you the way you conduct businessconduct business

Get yourGet yourinformation oninformation onthe Webthe Web

VVAA

LL

UU

EE

SUN ONE Smart Web Service

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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.

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WWW vs. Web Service

Web service supports dynamic interaction

HTMLHTML XMLXML

HTTPHTTP SOAPSOAP

HumanHuman MachineMachine

Language

Protocol

Reader

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The Elements of a Web Service

Key Players– The Service Provider– The Service Requester– The Service Registry

Key Functions– Publish– Find– Bound

Web Service

Web Service

Web Service

Service P

rovider

Service Register

Service Requester

Publish

Find

Bind

Service Net

Service Portal/ Engine

Service Provider

UDDI Registry

回應 (SOAP)

搜尋 (UDDI)

註冊 (WSDL)

Dynamic Request/ Rule Setting

Intelligent Mobile Delivery Service

CREDIT KM System

Workflow Service

Personalized Service

Classification Service

On-line Tracking Service

Mobile Web Service for CREDIT Center Mobile Web Service for CREDIT Center

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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.

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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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Service Requester

Service Provider

UDDI

WSDL

SOAP

搜尋 Web Service

註冊 Web Service

取得 Web Service 資訊

描述 Web Service

實際傳遞需求訊息

傳遞回應訊息

UDDI : Universal Description Discovery and Integration

WSDL: Web Service Description Language

SOAP : Simple Object Access Protocol

Web Service Mechanism

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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 Header

SOAP Body

SOAP Envelope

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WSDL

Web Services Description Language Proposed by Ariba, IBM, Microsoft WSDL is an XML format for describing ne

twork services– Binding– Interface

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UDDI

Harbour Metals createsonline website with local ASP

1.SydneyNet.com

Marketplaces and search enginesquery UBR, cache Harbour Metals data, and bind to its services

3. Consumers and businesses discover Harbour Metals and do business with it

4.

2.

ASP registersHarbour Metals with UBR

UDDI Registry

Semantic Web

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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

Ontology

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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 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.

Ontology Example

發佈、表示

導致、造成、帶來

氣象

影響

向、往帶來、引進

氣象報導 氣象百科 天文. . . . . .

寒流 颱風 降雨. . . . . .

. . . . . .

Relation

Association

中央氣象局 /氣象局

型態：預報人員、 天氣圖表示、警告、評估

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

來襲、形成、登陸

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、打雷發生、襲擊、增加

移動方向方向 :東方、南方 西北方、東 南方移動、靠近、前進

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

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、沙岸呈現、滯留、徘徊

災害型態 :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

氣壓型態 :副熱帶高氣 壓、熱帶性 低氣壓增強為、逼近

發生導致

造成

民眾 /人民型態 :人數注意、受困

提醒

時間型態 :最近、昨日、 今日、白天、 午後根據、開始

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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 class

es in a taxonomic (subclass-superclass) hierarchy.– Define attribute and describe allowed values for thes

e attribute.– Fill in the values for attribute for instance.

Ontology Learning Process

Knowledge Management System

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Enterprise Networking Resource

Non-structuredData

Internet/IntranetNews/Documents

XML-based E-documents

CMMI-based CREDITK.M. System

PersonalizedService

OntologyRepository

AutomaticClassification

Service

DocumentAbstraction

Service

DocumentRepository

WorkflowService

IntelligentMobile Delivery

Service

On-lineTrackingService

PersonalOntology

End User

OntologyConstruction

Service

MeetingScheduling

Service

SemanticSearchService

CMMIAssistantService

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CREDIT KM System Process Management

– Workflow → BPM + Web service– CMMI ( 中小企業 )– Mobile Workflow

Document Management– Knowledge Map– Q and A– FAQ– Personalization– Semantic Search– Knowledge Update

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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

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Maturity Level 2

Maturity Level 2

Process Area 1（ Requirement Management）

Process Area 2（ Project Planning）

Process Area 3（ Project Monitoring and Control）

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 represe

nt ontologies. Follow object-oriented analysis procedure t

o build ontologies. Apply natural language processing technol

ogy 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.

Structure of Object-Oriented OntologyDomain

Category 1 Category 2 Category k

Concept 3

Attributes 3

Operations 3

Concept 1

Attributes 1

Operations 1

Concept 2

Attributes 2

Operations 2

Concept n

Attributes n

Operations n

Concept 4

Attributes 4

Operations 4Class-layer

Instance 3

Attributes 3

Operations 3

Instance 1

Attributes 1

Operations 1

Instance 2

Attributes 2

Operations 2

Instance m

Attributes m

Operations m

Instance 4

Attributes 4

Operations 4Instance-layer

Association

Generalization

Aggregation

Instance-of

Concepts Class and Instance

+ ()改革+ ()發展

- ( ) : String州 省- : String城市- ( ) : String總統 元首、領導人- : String政黨- : String單位- : String媒體

國家

+ ()改革+ ()發展

- ( ) : String = 州 省 加州、德州- : String = 城市 紐約、華盛頓、費城、舊金山、芝加哥、洛杉磯- : String = 總統 布希- : String = 政黨 共和黨、民主黨- : String = 單位 白宮、五角大廈、聯邦調查局、太空總署、國務院- : String = CNN媒體 華盛頓郵報、

( )美國 美方、美利堅合眾國

Domain Ontology Construction

Episodes

DocumentPre-processing

DomainOntology

Nouns

Sentences

Concepts

Concept Clustering

Episode Extraction

Attributes, Operations,Associations Extraction

DAML+OIL Format

Special Domain Documents

ChineseDictionary

Data Flow

Control Flow

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Stop Word Filter

Nouns/Verbs

Repository

Episode NetRepository

Episode Net Extractor

EpisodeExtractor

ConceptExtractor

Attributes-Operation- Association

Extractor

EpisodesRepository

ConceptsRepository

Chinese Domain Ontology

…

Ontology Construction Agent

English Domain Ontology

…

Common Data Flow

Chinese Data Flow

English Data Flow

Part-Of-Speech Tagger

Domain Term Combination Process

er

InputDocuments

HowNet WordNet

ChineseTerm

Dictionary

EnglishTerm

Dictionary

Knowledge Base

Genetic Learning

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Episodes Extractor

An episode is a partially ordered collection of events occurring together.

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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。

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)

( 德國 , Nc, 1) 　 ( 門將 , Na, 2) 　 ( 卡恩 , Nb, 3) 　 ( 贏得 , VJ, 4)　 ( 世足賽 , Nb, 5) 　 ( 代表 , Na, 6) 　 ( 球員 , Na, 7) 　 ( 金球獎 , Nb, 8)

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

POS Tagger

Stop Word Filter

Episode Extractor

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Internet

e-News

RetrievalAgent

Fuzzy InferenceAgent

Chinese e-News Summar

y

Chinese e-NewsOntology

…

Chinesee-News

SummaryRepository

Real-time e-News

Repository

e-News Repository

GUI

POS Tagger(CKIP)

Chinese Term Filter

Document Processing Agent

OFEE Agent

Extracted-EventOntology

PDA

Cell Phone

Notebook

Event Ontology Filter

SentenceRule Base

Sentence Generation

Agent

Summarization Agent

Document Abstraction Agent

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Semantic Search

Human-readable– HTML

Machine-readable– XML

Machine-understandable– Semantic Web with Ontology (RDF,DAML+OIL)

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Semantic Search

Keyword-based search– Single-word query– Context query– Boolean query

Conceptual search– Conceptual query– Natural language query

Semantic search– Ontology-reasoning query

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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 "wha

t they say" The user who has no idea about domain te

rminologies can’t find information easily.

XML fileRepository

Index Repository

PersonalThesaurusRepository

OntologyRepository

CKIPRepository

RepositoryWWW

InformationRetrieval

Agent

Indexing and Gathering statistics

Natural LanguageProcessing

Query

Query Inference

Query Personalization

Query Results

End User

Parsing and Transforming formats

Clustering

Document Preprocessing Query processing

Semantic Search Architecture

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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

Question & Answer System

QuestionOntology

Question

WhereWhat How

AnswerOntology

KM

PMworkflow Q&A

KM

workflowOntology Q&ASearchengine

OntologyDomain

Knowledge extraction & learning process

Question Answering Subsystem

Knowledge Extraction Subsystem

Receive User query

Documents

Return Answer

Ontology supervision

Question & Answer Knowledge Base

Answering processUser query process

User

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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

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Question & Answer Knowledge Base Alternation Rule

– Morphological – Lexical – Semantic

Ontology supervision– Ontology management– Ontology inference

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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.

<<History>>

User UsedBehavior

Functionalization

End User

WWW

Personal Log Files

Repository

BrowsedContents

Repository

Log FileRecording

Engine

Web ContentRecording

Engine

Function ofBrowsingFrequency

Function ofBrowsing

Time

FuzzyInference

<<Present>>

PreferenceDegree

ContentConceptualization

&Weighting

<<Present>>

Present BrowsingBehavior

Concepts and Weightsof Browsed

Content

Sequence of User Browsing Behavior

s1 s2 s3 sn…

User Behavior & Browsed Content Analysis

DomainOntology

User Behavior & Browsing Content Analysis

…… … … …Additional Weighting of

Related Sequence Concepts

DomainOntology

PersonalOntology

Personal Ontology

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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 log

time

length

BrowsingFrequency

BrowsingTime

ContentLength

Personal Log Files

Repository

BrowsedContents

Repository

FeatureData

Data Sorting

…

Data Clustering

SortedData

FunctionalizationFunction

of FeatureData

Feature Data Functionalization

Feature DataProcessing

FrequencyFeature Data

Browsing TimeFeature Data

Feature Data Functionalization

Function ofBrowsingFrequency

Function ofBrowsing

Time

Personal Ontology

Meeting Scheduling Architecture

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The Architecture of Fuzzy Inference Agent

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The Flow Chart of Genetic Learning Agent

工作流程

部門角色

文件

管理者使用者

新增、刪除修改架構

新增、刪除修改流程

組織架構

人員( 人事部 )

載入、更新部門角色

簽核

發文

加簽 定義會簽

文件控管與分析

定義職務代理人

新增、刪除修改角色權限

定義角色權限

組織設計師

流程設計師

流程管理員

工作管理員

Workflow Engine

解析流程

記錄變動

執行多條流程

logs

DB

Workflow Process

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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.