建构CMMI知识地.ppt

建構CMMI知識地圖,李健興 長榮大學資訊管理系副教授 兼資訊工程系籌備處主任 2004/11/17,2,Outline,Introduction The Structure of Ontology Ontology-based Knowledge Management System Ontology Construction CMMI Ontology CMMI Assistant Tools CMMI Ontology Extraction Future Works,Introduction,4,Ontology (知識地圖),The ontology is a collection of key concepts and their interrelationships collectively providing an abstract view of an application domain. An ontology is a formal, explicit specification of a shared conceptualization. Conceptualization Explicit Formal,5,Ontology (知識地圖),Ontologyexplicit 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.,6,Ontology (知識地圖),The main application areas of ontology technology Knowledge management Web commerce Electronic business Database design Natural language processing Multi agent system ,7,飛機,航空公司: 班機號碼: 時間: 速度: 價格:,交通資訊,台北台南,台北高雄,台北澎湖,自行開車,路線: 時間:,火車,班次: 車種: 時間: 速度: 價格:,搭巴士,巴士公司: 路線: 時間: 價格:,Example,搭船,船公司: 路線: 時間: 價格:,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,. . . . . .,. . . . . .,發生,導致,造成,提醒,9,DAML+OIL format, ,10,Characteristics of Ontology,Formal Semantics Consensus of terms Machine readable and processable Model of real world Domain specific,11,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.,12,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.,13,Ontology Learning Process,The Structure of Ontology,15,The three-layered object-oriented ontology,Domain,Category 1,Category 2,Category 3,Category k,Concepts Set,Association,Generalization,Aggregation,16,The four-layered Object-Oriented Ontology,17,The four-layered News Ontology (cont.),18,The four-layered News Ontology,發佈、 表示,導致、造成、帶來,氣象,影響,向、往 遠離、移動,帶來、 引進,氣象報導,氣象百科,天文,. . . . . .,寒流,颱風,降雨,. . . . . .,. . . . . .,Relation,Association,表示、警告、評估,型態：預報人員、 天氣圖,中央氣象局/氣象局,來襲、形成、登陸,編號:*(Neu)號 中心位置：:*(Nc)(Ncd)(Neu)(Nf) 強度:輕度颱風 型態:暴風圈,颱風,發生、襲擊、增加,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,降雨,移動、靠近、前進,方向:東方、南方 西北方、東 南方,移動方向,接近、影響、流動,型態:西南氣流、 冷氣流,氣流,避風、休耕,型態:漁港、農田 、農作物、 魚貨量,農林漁牧業,呈現、滯留、徘徊,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,地區,來襲、形成、登陸,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,災害,增強為、逼近,型態:副熱帶高氣 壓、熱帶性 低氣壓,氣壓,發生,導致,造成,注意、受困,型態:人數,民眾/人民,提醒,根據、開始,型態:最近、昨日 今日、白天 午後,時間,影響,恢復,出現、發生,19,Fuzzy Ontology (cont.),Domain,Category 2,C : Concept A : Attribute O : Operation,Category 1,Category 3,Category k,Class-layer,C1;C1E1,C1E2,C1Ep,AC11,AC12 ,AC1q1,Cm;CmE1,CmE2,CmEp,ACm1,ACm2,ACmqm,OCm1 ,OCm1,OCmqm,C2;C2E1,C2E2,C2Ep,AC21,AC22 ,AC2q2,C3;C3E1,C3E2,C3Ep,AC31,AC32 ,AC3q3,C4;C4E1,C4E2,C4Ep,AC41,AC42,AC4q4,OC41,OC41,OC4q4,C5;C5E1,C5E2,C5Ep,AC51,AC52,AC5q5,OC51,OC51,OC5q5,Association,Event E1,Event E2,Event E3,Event Ep,OC11 ,OC11,OC1q1,OC21 ,OC21 ,OC2q2,OC31 ,OC31 ,OC3q3,LBR,LNR,20,Fuzzy Ontology,Ontology-based Knowledge Management System,22,CREDIT Research Center,Located at National Cheng Kung University. Supported by Walsin Lihwa Group. (2001-2004) Contain three main research groups. More than 10 professors and 50 Ph.D or master students.,23,CREDIT KM System (cont.),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,24,CREDIT KM System,Meeting Management Meeting Scheduling Meeting Notification Meeting Follow-up Message Management BBS Notification Directory Service for Message Delivery,26,Semantic Search Service(cont.),Human-readable HTML Machine-readable XML Machine-understandable Semantic Web with Ontology (RDF,DAML+OIL),27,Semantic Search Service,Keyword-based search Single-word query Context query Boolean query Conceptual search Conceptual query Natural language query Semantic search Ontology-reasoning query,28,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 cant find information easily.,XML file Repository,Index Repository,Personal Thesaurus Repository,Ontology Repository,CKIP Repository,Repository,Information Retrieval Agent,Indexing and Gathering statistics,Natural Language Processing,Query,Query Inference,Query Personalization,Query Results,End User,Parsing and Transforming formats,Clustering,Document Preprocessing,Query processing,Semantic Search Service Architecture,30,Personalized 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.,Personal Ontology,32,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,Personal Ontology,34,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,Question & Answer System,36,Question & Answer Knowledge Base (cont.),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,37,Question & Answer Knowledge Base,Alternation Rule Morphological Lexical Semantic Ontology supervision Ontology management Ontology inference,Internet,e-News,Retrieval Agent,Fuzzy Inference Agent,Chinese e-News Summary,Chinese e-News Ontology,Chinese e-News Summary Repository,Real-time e-News Repository,e-News Repository,G U I,POS Tagger (CKIP),Chinese Term Filter,Document Processing Agent,OFEE Agent,Extracted-Event Ontology,Notebook,Event Ontology Filter,Sentence Rule Base,Sentence Generation Agent,Summarization Agent,Document Abstraction Service,Meeting Scheduling Service,Meeting Host,Meeting Scheduling Decision Support System (MSDSS),Group Calendar Data Base (GCDB),Genetic Learning Agent (GLA),Fuzzy Inference Agent (FIA),Meeting Information Knowledge Base (MIKB),Personalized Knowledge Base (PKB),Evaluation Module,Meeting Negotiation Agent (MNA),user names,proper time with work priority,Invitees Devices,Cell Phone,PDA,Notebook,Desk Computer,IFA,40,The Architecture of Fuzzy Inference Agent,41,The Flow Chart of Genetic Learning Agent,Workflow Service,Ontology Construction,44,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.,45,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.,46,Structure of Object-Oriented Ontology,47,Concepts Class and Instance,Specific Class News Documents (Training Data),Part-of-speech Tagger (CKIP),Nouns Set,Verbs Set,Chinese Electronic Dictionary,Segmentation Standard Dictionary,Academia Sinica Balanced Corpus,Term Analyzer,Data Mining,Concepts Clustering Processing,Association Rule Result,Chinese Electronic Dictionary,Academia Sinica Balanced Corpus,Concepts Construction Agent,Operations Construction Agent,Attributes Construction Agent,Relations Construction Agent,Ontology Construction Procedure,Domain Ontology,Concepts Set,Domain Ontology Construction (I),Refining Tagging,Stop Word Filter,Domain Ontology Construction(II),Episodes,Document Pre-processing,Domain Ontology,Nouns,Sentences,Concepts,Concept Clustering,Episode Extraction,Attributes, Operations, Associations Extraction,Chinese Dictionary,Feature Term Pre-processor,Episode Net Extractor,Episode Extractor,Concept Extractor,Attributes-Operation- Association Extractor,Chinese Domain Ontology,Ontology Construction Agent,Domain Term Combination Processor,Knowledge Base,Part-Of-Speech Tagger,Data Flow,Chinese Documents,Control Flow,Domain Expert,Domain Ontology Construction(III),51,Episode Extractor (cont.),An episode is a partially ordered collection of events occurring together.,52,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,Episode Extractor,The following shows an example of extraction of episode from a sentence.,CMMI Ontology,54,The definition of CMMI,The CMMI, Capability Maturity Model Integrated, is a model for improving organizations processes and ability to manage the development, acquisition, and maintenance of products of services.,55,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）,Maturity Level 2,57,CMMI Level 2 Ontology,進度監控記錄表,階段：String 工作項目名稱：String 預計完成日期：String 實驗完成日期：String 完成百分比：Integer 未完成理由：String,度量分析報告,各表單參數之輸入：String 度量分析後結果：String,工作產品與工作項目清單,工作產品編號：String 工作產品名稱：String 工作項目編號：String 工作項目名稱：String 數量：String 工作產品製作：String 估計項目/估計值：String 負責人：String,完成 百分比,工作 項目 名稱,工作項目名稱,58,Specific Practice Form,進行進度 審查 SP1.6,59,Semantic CMMI Ontology (cont.),Domain,C : Concept A : Attribute,Category 1,Class-layer,Category 2,Category n,C2,AC21:VC211,VC212 ,VC21R1 AC22 :VC221,VC222 ,VC22R2 AC2K1:VC2K11,VC2K12 ,VC2K1RK1,62,Document Management for CMMI,Ontology,Repository,Document,Repository,流程管理中心,1.P3,自動會議排程,1.S1,訊息管理中心,1.P5,支援CMMI Level 2認證之文件管理平台,Administrator,End User,Edit,差異分析工具,1.P1,專案管理中心,1.P2,全文檢索,1.S3,文件管理中心,1.P4,問答知識庫,1.S2,度量分析工具,1.S4,企業組織 的表單,63,Find Measurement and Analysis Service,PFA-WS,WSDL,WSDL,WSDL,WSDL,Project Planning Ontology Repository,Project Monitor Ontology Repository,Supplier Agreement Management Ontology Repository,Process and Product Quality Assurance Ontology Repository,Basic Web Services,Composite Services,Measurement & Analysis data,SOAP,Composed of,ODBC,Web Client,PPMA-WS,PSPA-WS,PBMA-WS,PCAA-WS,SSS-WS,WPQAS,PFA-WS,PCAA-WS,PFA-WS,WPQA-WS,PBMA-WS,PSPA-WS,PCAA-WS,SSS-WS,WPQA-WS,Measurement & Analysis,64,Monitor,Project,Repository,Monitor Supplier,Agreement Management,Repository,Basic Web Services,UDDI,PPMS,PRMS,PCAMS,PSIMS,SPMS,PDQMS,PCQMS,Process and Product,Quality Assurance,Repository,PSIMS,PPMS,SPMS,PRMS,PDQMS,PCAMS,PCQMS,SPMS,PPMS,PRMS,PPMS,PCQMS,UDDI,Web,Client,Composite,Services,WSDL,Service,Registry,WSDL,WSDL,SOAP,Composed of,Project Monitor Service,Supplier Agreement,Management Monitor Service,Process and Product,Quality Monitor Service,JDBC,Find Project Monitor,and Control Service,Project Monitor,and Control,Data,Project Monitor & Control,CMMI Assistant Tools,66,CMMI Assistant Tools with SIM,The purpose of CMMI Assistant Tools is to provide computation support for improving processes of software organization and managing the development, acquisition, and maintenance of products or services.,67,Arc