Ontology-based Knowledge Management System for CREDIT Research Center

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Ontology-based Knowledge Management System for
CREDIT Research Center

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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 master
  students.

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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

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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

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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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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

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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
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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
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• 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 concepts
  property through an attribute-value mechanism.
• Relations between concepts describe additional
  logical sentence.

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Ontology Example
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DAMLOIL format
lt?xml version1.0 encodingBig5?gt ltrdfRDF xmlnsrdf http//www.w3.org/1999/02/22-rdf-syntax-ns xmlnsrdfshttp//www.w3.org/2000/01/rdf-schema xmlnsdamlhttp//www.daml.org/2001/03/damloil xmlnsxsd http//www.w3.org/2000/10/XMLSchema xmlnsa http//.stanford.edu/system gt ltdamlOntology rdfabout??gt ltdamlimports rdfresourcehttp//www.daml.org/2001/03/damloil /gt lt/damlOntologygt ltdamlClass rdfID??gt lt/damlClassgt ltdamlClass rdfID????gt ltdamlrange rdfresource ??/gt lt/damlObjectPropertygt ltdamlObjectProperty rdfID??gt ltdamldomain rdfresource ??/gt ltdamlrange rdfresource ?????/gt lt/damlObjectPropertygt lt/rdfRDFgt
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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
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Knowledge Management System
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CREDIT KM System

• Process Management
• Workflow ? BPM Web service
• CMMI (????)
• 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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??CMMI

• Capability Maturity Model Integrated
  (CMMI)???????1991??????????????????????????,??????
  ????/???????????????????

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Maturity Level 2
Process Area 1(Requirement Management)
Process Area 2(Project Planning)
Maturity Level 2
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 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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Structure of Object-Oriented Ontology
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Concepts Class and Instance
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Domain Ontology Construction
Document Pre-processing
Nouns
Chinese Dictionary
Concept Clustering
Sentences
Episode Extraction
Concepts
Attributes, Operations, Associations Extraction
Episodes
Domain Ontology
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Common Data Flow
Ontology Construction Agent
InputDocuments
Part-Of-Speech Tagger
Nouns/ Verbs Repository
Stop Word Filter
Chinese Data Flow
Concept Extractor
Concepts Repository
English Data Flow
Domain Term Combination Processer
Episode Extractor
Episodes Repository
Episode Net Extractor
Chinese Term Dictionary
English Term Dictionary
Genetic Learning
Episode Net Repository
HowNet
WordNet
Attributes-Operation- Association Extractor


Knowledge Base
Chinese Domain Ontology
English Domain Ontology
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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

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POS Tagger
??(Nc) ??(Na) ??(Nb) ??(VJ) ?(Nes) ?(Nf) ???(Nb)
??(Na) ??(A) ??(Na) ?(DE) ???(Nb)?(PERIODCATEGORY)

Stop Word Filter
(??, Nc, 1) (??, Na, 2) (??, Nb, 3) (??, VJ, 4)
(???, Nb, 5) (??, Na, 6) (??, Na, 7) (???, Nb, 8)
Episode Extractor
??(Nc)_??(Na)_??(Nb) Germany_keeper_Oliver
Kahn ??(Nb)_??(VJ)_???(Nb) Oliver
Kahn_took_Golden Ball
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Document Abstraction Agent
G U I
Internet
OFEE Agent
Document Processing Agent
Retrieval Agent
e-News
Real-time e-News Repository
POS Tagger (CKIP)
Fuzzy Inference Agent
Chinese Term Filter

Event Ontology Filter
Chinese e-News Summary Repository
Chinese e-News Ontology
Summarization Agent
Extracted-Event Ontology
e-News Repository
Chinese e-News Summary
Sentence Rule Base
Sentence Generation Agent
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Semantic Search

• Human-readable
• HTML
• Machine-readable
• XML
• Machine-understandable
• Semantic Web with Ontology (RDF,DAMLOIL)

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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. ?? v.s.
  ????)
• 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 cant find information easily.

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Question Answer System

• Question analysis
• 5W1H
• what, who, when, where, why, and how.
• Indirectly question other
• YesNo questionetc.
• Answer analysis
• Question type
• 5W1H
• Domain
• Domain knowledge

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Question Answer System
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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 QA 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.

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Personal Ontology
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User Behavior Analysis

• In order to find out users 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

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Personal Ontology
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Meeting Scheduling Architecture
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The Architecture of Fuzzy Inference Agent
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The Flow Chart of Genetic Learning Agent
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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.