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Ontology Technology and Its Applications on the Internet

Ontology Technology and Its Applications on the Internet

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Ontology Technology and Its Applications on the Internet

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  1. Ontology Technology and Its Applications on the Internet 李健興 長榮大學資訊管理系

  2. Outline • Web Service • Semantic Web • Ontology • Knowledge Management System • Some Applications on the Internet • Conclusion

  3. Web Service

  4. The Evolution Of E-business WebServices Commerce Transact Leverage your business experience Collaboration V A Accessdata L Transform the way you U conduct business E Publish Integrate the Web with business systems Get your information on the Web Security Business Chasm Chasm

  5. SUN ONE Smart Web Service

  6. 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. Reader Human Machine Language HTML XML Protocol HTTP SOAP WWW vs. Web Service • Web service supports dynamic interaction

  8. Publish Service Provider Service Register Find Bind Service Requester Web Service Web Service Web Service The Elements of a Web Service • Key Players • The Service Provider • The Service Requester • The Service Registry • Key Functions • Publish • Find • Bound

  9. Web Services Can be • Described • Published • Found • Bound • Invoked • Composed

  10. 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.

  11. Examples of Web Services • Business process externalization: business linkages at the workflow level, net marketplace, extended supply chains. • E-government • E-learning • Digital library

  12. 搜尋Web Service ServiceRequester ServiceProvider UDDI 註冊Web Service 取得Web Service資訊 WSDL 描述Web Service 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 Web Service Mechanism

  13. SOAP Message HTTP Header SOAP Envelope SOAPHeader SOAPBody 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

  14. WSDL • Web Services Description Language • Proposed by Ariba, IBM, Microsoft • WSDL is an XML format for describing network services • Binding • Interface

  15. 1. UDDI Registry Harbour Metals createsonline website with local ASP 2. 4. ASP registersHarbour Metals with UBR 3. Consumers and businesses discover Harbour Metals and do business with it Marketplaces and search enginesquery UBR, cache Harbour Metals data, and bind to its services UDDI

  16. Semantic Web

  17. 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.

  18. 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)

  19. 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

  20. Architecture of Semantic Web

  21. 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.

  22. 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).

  23. Language stack in Semantic Web

  24. Ontology

  25. Ontology • A Revolution for Information Access and Integration. • An ontology is a formal, explicit specification of a shared conceptualization. • Conceptualization • Explicit • Formal

  26. Ontology • The main application areas of ontology technology • Knowledge management • Web commerce • Electronic business

  27. 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.

  28. Relation Association Ontology Example 氣象 氣象報導 氣象百科 天文 . . . . . . 寒流 颱風 降雨 . . . . . . . . . . . . 造成 導致、造成、帶來 發佈、 表示 提醒 發生 向、往 導致 帶來、引進 影響

  29. DAML+OIL format

  30. Characteristics of Ontology • Formal Semantics • Consensus of terms • Machine readable and processable • Model of real world • Domain specific

  31. 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.

  32. 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.

  33. Ontology Learning Process

  34. Knowledge Management System

  35. Internet/Intranet News/Documents Document Repository Enterprise Networking Resource End User CMMI Assistant Service Meeting Scheduling Service Workflow Service Semantic Search Service On-lineTracking Service Non-structuredData CMMI-based CREDIT K.M. System Intelligent Mobile Delivery Service XML-based E-documents Document Abstraction Service Personalized Service Ontology Construction Service Automatic Classification Service Ontology Repository Personal Ontology

  36. 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

  37. CREDIT KM System • Meeting Management • Meeting Scheduling • Meeting Notification • Meeting Follow-up • Message Management • BBS • Notification • Directory Service for Message Delivery

  38. 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

  39. 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)

  40. 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.

  41. 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.

  42. Aggregation Generalization Domain Ontology Domain …………… Category 1 Category 2 Category 3 Category k Association Event E1 Event E2 Event E3 …… Event Ep C : Concept A : Attribute O : Operation 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 ACm1,ACm2,…,ACmqm …… OC41,OC41,…,OC4q4 OC51,OC51,…,OC5q5 OCm1 ,OCm1,…,OCmqm Class-layer

  43. Concepts Class and Instance

  44. DAML+OIL / OWL Format Special Domain Documents Data Flow Control Flow Domain Ontology Construction Document Pre-processing Nouns Chinese Dictionary Concept Clustering Sentences Episode Extraction Concepts Attributes, Operations, Associations Extraction Episodes Domain Ontology

  45. 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

  46. Episodes Extractor • An episode is a partially ordered collection of events occurring together.

  47. 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)(世足賽, Nb, 5)(代表, Na, 6)(球員, Na, 7)(金球獎, Nb, 8) 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

  48. Document Abstraction Agent PDA Cell Phone Notebook G U I OFEE Agent Internet 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