Ontology-based Knowledge Management System for CREDIT Research Center

1. 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

3. 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.

4. Web Service

5. 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

7. SUN ONE Smart Web Service

8. 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.

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

10. 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

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

13. 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.

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

15. 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

16. 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

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

18. 1. SydneyNet.com 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

19. Semantic Web

20. 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.

21. 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)

22. 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

23. Architecture of Semantic Web

24. 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.

25. 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).

26. Language stack in Semantic Web

27. Ontology

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

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

30. 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.

31. Relation Association 氣象 Ontology Example 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

32. DAML+OIL format

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

34. 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.

35. 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.

36. Ontology Learning Process

37. Knowledge Management System

38. 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

39. 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

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

41. 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

42. 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）

43. 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.

44. 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.

45. Generalization Aggregation Domain Category 1 Category 2 Category k Association Instance-of Class-layer Instance m Instance 3 Instance 1 Instance 4 Concept n Instance 2 Concept 2 Concept 3 Concept 1 Concept 4 Attributes m Attributes 3 Attributes 4 Attributes 2 Attributes 4 Attributes 1 Attributes 2 Attributes 3 Attributes 1 Attributes n Operations 2 Operations 4 Operations 3 Operations m Operations 4 Operations 1 Operations 3 Operations n Operations 1 Operations 2 Instance-layer Structure of Object-Oriented Ontology

46. Concepts Class and Instance

47. DAML+OIL 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

48. 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

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

50. 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