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題名 以知識本體為基礎的壽險客服應用
Ontology Based CRM for Life Insurance Company
作者 簡月秀
Chien, Yueh-hsiu
貢獻者 劉文卿
Liou, Wen-chin
簡月秀
Chien, Yueh-hsiu
關鍵詞 知識本體
知識整合工具
人壽保險
客服中心
Ontology
Knowledge Acquisition
Protege2000
KAON
日期 2002
上傳時間 11-Sep-2009 17:59:55 (UTC+8)
摘要 依財政部保險司每半年對壽險申訴案件的統計觀察，客戶對壽險公司提出申訴的件數有逐年遞增的趨勢，申訴原因多為理賠給付、契約保全服務、業務員招攬、保費收費糾紛、商品條款及法規等爭議。客服員若能於第一時間以專業的知識回應申訴問題，勢必可減少申訴數量。但面對浩瀚的壽險各領域知識，客服中心的成員不易具備所有足以應付客戶問題的知識，實施知識管理將組織內各種知識公開化、標準化、制度化、科技化，已成為刻不容緩的任務。
      為解決壽險客服中心因取得壽險專業知識的障礙而無法提供客戶滿意的服務，本研究對客服中心可能應用的各種知識，探討已被發表的知識管理分類、呈現與檢索等應用的相關技術，提出符合客服中心需求的以知識本體為基礎的壽險知識問題檢索架構，除了提供壽險專業知識的建立、搜尋、分享環境，並將原始文件儲存為XML或RDF的語意文件，做為將來跨平台與跨應用系統資料交換的基礎，提升客服中心服務效率、節省客服人力與行政成本，留住忠誠的客戶以達成CRM的目的，進而提升企業競爭力。
According to the regular semi-annual statistic report from Insurance Department of MOF Taiwan, the complain cases from policy owners are increasing year by year. The reasons of complain are claims, underwriting, policy services, marketing, premium collections, policy clauses and regulations. If the customer service representatives (CSR) could solve the problems at the right time with their domain knowledge of life insurance, the volumes of complain should be descended explicitly. But it is hard to ask a CSR to play the role with variety domain knowledge of life insurance. Implement the knowledge management mechanism to standardize and enrich the organization memory are the first priority in life insurance companies.
     In order to overcome the barrier of dissatisfaction about customer service, this research try to study the related theories and technologies of knowledge classification, knowledge representation, knowledge acquisition tools, inference and problem solving methods. The results of this research announce an architecture of ontology based customer relationship management for Life Insurance Company to build, represent, search, and share the life insurance domain knowledge. Addition, to save the original semi-structured or unstructured documents into semantic formats of XML or RDF. It could provides the data exchange between heterogeneous databases and platforms. The advantages are more efficiency and cost down for the customer service departments, retain the royalty customers and get more competence advantage for the company.
"目 錄
     
     
     第一章 緒論 1
     第一節　研究動機 1
     第二節　研究目的 2
     第三節　預期貢獻 2
     第二章 文獻探討 4
     第一節　知識本體(ONTOLOGY) 4
     2-1-1　知識本體的定義 4
     2-1-2　知識本體的應用 4
     2-1-3　知識本體的種類 6
     2-1-4　知識本體方法論 7
     2-1-5　知識本體語言（Ontology Language） 15
     第二節　語意網（SEMANTIC WEB） 19
     第三節　知識整合工具（KNOWLEDGE ACQUISITION TOOL） 20
     2-3-1　Protégé-2000 20
     2-3-2　KAON語意網架構（The KArlsruhe ONtology and Semantic Web framework） 24
     2-3-3　知識本體為基礎的企業知識管理系統架構 26
     2-3-4　知識本體工具比較 30
     第四節　知識管理 31
     第三章 研究方法與架構 35
     第一節　研究方法 35
     3-1-1　文獻分析法 35
     3-1-2　深度訪談 42
     3-1-3　領域分析 43
     第二節　研究範圍與限制 49
     3-2-1　研究範圍 49
     3-2-2　研究限制 49
     第三節　研究架構 50
     第四章 以知識本體為基礎的壽險客服應用 51
     第一節　壽險客服知識分類 51
     第二節　建立客服知識本體 52
     4-2-1　文件知識本體 52
     4-2-2　壽險領域知識本體 56
     4-2-3　系統架構 57
     4-2-4　系統實作 59
     第五章 結論 70
     第一節　研究成果與貢獻 70
     第二節　未來發展 70
     第六章 參考文獻 72
     英文部份 72
     中文部份 76
     
     
     
     圖　目　錄
     
     圖1-1：人壽保險申訴案件統計 2
     圖2-1：知識本體應用示意圖 5
     圖2-2：知識本體的種類 6
     圖2-3：各知識本體再利用性與實用性比較 7
     圖2-4：Bernaras等Top-level Ontology Methodology 8
     圖2-5：Bernaras等知識本體再利用方法論 9
     圖2-6：SENSUS Ontology Methodology 9
     圖2-7：METHONTOLOGY Framework 10
     圖2-8：Life Cycle of METHONTOLOGY 12
     圖2-9：Conceptualisation of METHONTOLOGY 12
     圖2-10：知識本體導入流程 15
     圖2-11：RDF 基本資料模式 16
     圖2-12：使用RDF圖形描述知識本體 17
     圖2-13：The Semantic Web layers 20
     圖2-14：Protégé-2000發展演進時程 21
     圖2-15：Protégé-2000架構圖 22
     圖2-16：Protégé’s class editor 23
     圖2-17：Protégé’s slots editor 24
     圖2-18：德國Karlsruhe知識本體架構 25
     圖2-19：在Ontology-interface的知識本體演化圖 26
     圖2-20：Ontology-based知識管理系統架構圖 27
     圖2-21：知識本體對應處理流程圖 29
     圖3-1：知識本體發展程序圖 35
     圖3-2：酒與水果概念圖 36
     圖3-3：多重繼承概念圖 37
     圖3-4：酒的相關概念與屬性 38
     圖3-5：子概念與父概念屬性繼承關係 38
     圖3-6：一個以上的父概念 40
     圖3-7：Disjoint Classes 40
     圖3-8：概念循環 41
     圖3-9：Ontology is often just the beginning 42
     圖3-10：個案公司CTI話術畫面樹狀圖 42
     圖3-11：研究架構圖 50
     圖4-1：文件知識本體 52
     圖4-2：上下從屬繼承關係向下查詢 53
     圖4-3：上下繼承關係向上查詢 54
     圖4-4：參照關係 55
     圖4-5：簡單概念與複合概念 56
     圖4-6：壽險知識本體 57
     圖4-7：概念語意查詢系統架構圖 58
     圖4-8：文件知識本體 60
     圖4-9：壽險知識本體 61
     圖4-10：聯繫函屬性值 62
     圖4-11：期刊屬性值 63
     圖4-12：以父層概念屬性條件查詢 64
     圖4-13：所有概念其文件產生者屬性包含『保戶服務部』查詢結果 65
     圖4-14：限定文章概念查詢條件之查詢結果 65
     圖4-15：查詢結果文件詳細內容 66
     圖4-16：聯繫函之XML格式 67
     圖4-17：聯繫函概念與屬性之RDF格式 68
     圖4-18：聯繫函屬性值之RDFs格式 69
     
     
     
     
     
     
     表　目　錄
     
     表2-1：知識本體工具比較 31
     表3-1：商業模式的知識構成要素 45
     表3-2：職業代碼表 46
     表3-3：商學網保險知識分類表 47
     表3-4：聯合理財網保險知識分類表 48
     表4-1：壽險詞彙 57"
參考文獻 參考文獻
英文部份
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[2]Swartout, William, and Tate, Austin, Ontologies, IEEE Intelligent Systems, (14:1), January/February 1999, pp. 18-19.
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[7]Asunción Gómez-Pérez, ONTOLOGICAL ENGINEERING, 1999, Tutorial on Ontological Engineering: IJCAI’99
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[9]B. Swartout; R. Patil; k. Knight; T. Russ. Toward Distributed Use of Large-Scale Ontologies, Ontological Engineering, AAAI-97 Spring Symposium Series, 1997, pp138-148
[10]Gómez-Pérez, A. Knowledge Sharing and Reuse, In the Handbook of Applied Expert Systems, CRC Press, 1998.
[11]Grüninger, M.; Fox, M.S. Methodology for the Design and Evaluation of Ontologies, IJCAI Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, 1995
[12]speel, H.; Raalte, F; Vet, P.; Mars, N. Scalability of the Performance of Knowledge Representation Systems, Towards Very Large Knowledge Bases, Ed. by N. Mars., IOS Press, Amsterdam, 1995, pp173-184
[13]Farquhar, A.; Fikes, R.; Pratt, W.; Rice, J., Collaborative Ontology Construction for Information Integration, Technical Report KSL-95-10, Knowledge Systems Laboratory. Stanford University, CA, 1995
[14]Gómez-Pérez, A.; Juristo, N.; Pazos, J., Evaluation and Assessment of Knowledge Sharing Technology, Towards Very Large Knowledge Bases, Ed. by N. Mars. IOS Press, Amsterdam. 1995, pp289-296
[15]Gómez-Pérez, A., A Framework to Verify Knowledge Sharing Technology, Expert Systems with Applications, Vol. 11, N.4. 1996, pp519-529
[16]Gangemi, A.; Steve, G.; Giacomelli, F., ONIONS: An Ontological Methodology for Taxonomic Knowledge Integration, Workshop on Ontological Engineering, ECAI`96, Budapest. Hungary, pp29-40.
[17]Skuce, D. Viewing Ontologies as Vocabulary:Merging and Documenting the Logical and Linguistic Views, IJCAI Workshop on Basic Ontological Issues in Knowledge Sharing, Montreal, Quebec, Canada. 1995.
[18]Fernández, M.; Gómez-Pérez, A.; Pazos, J.; Pazos. A., Building a Chemical Ontology using methontology and the ontology desing environment., IEEE Intelligent Systems and their applications, 1999, !4 (1):37-45
[19]M. Blázquez; M. Fernández; J. M. García-Pinar; A. Gómez-Pérez., Building Ontologies at the Knowledge Level, Sharable and reusable components for knolwedge systems, Banff KAW98, 1998
[20]Decker S., F.V. Harmelen, J. Broekstra, M. Erdmann, D. Fensel, I. Horrocks, M. Klein, and S. Melnik, The Semantic Web- on the respective Roles of XML and RDF, 2000
[21]Resource Description Framework (RDF): Concepts and Abstract Syntax, http://www.w3.org/TR/2003/WD-rdf-concepts-20030123/#section-Concepts, 23 Jan. 2003
[22]RDF Vocabulary Description Language 1.0: RDF Schema, http://www.w3.org/TR/2003/WD-rdf-schema-20030123/#ref-rdf-concepts, 23 Jan. 2003
[23]Natalya F. Noy, Michael Sintek, Stefan Decker, Monica Crubézy, Ray W. Fergerson, and Mark A. Musen, Stanford University, Creating Semantic Web Contents with Protégé-2000, IEEE Computer Society, Computer.org/Intelligent, March/April 2001, p.61, http://www-smi.stanford.edu/pubs/SMI_Reports/SMI-2001-0872.pdf
[24]DAML+OIL (March 2001) Reference Description, W3C Note 18 December 2001, http://www.w3.org/TR/2001/NOTE-daml+oil-reference-20011218
[25]Tracy K.W., and B. Peter, Object-Oriented Artificial Intelligence Using C++, W.H. Freeman And Company, 1997
[26]Kifer M., G. Lausen, and W. James, Logical foundations of object-oriented and frame-based languages, Journal of ACM, 1995
[27]Fensel D., The Semantic Web and Its Language, IEEE Intelligence System, Vol. 15, No. 6, pp. 67-73, Nov./Dec. 2000
[28]Marja-Riitta Koivunen, Eric Miller, W3C Semantic Web Activity, Semantic Web Kick-off Seminar in Finland Nov 2, 2001, http://www.w3.org/2001/12/semweb-fin/w3csw
[29]Holger Knublauch, An AI tool for the real world- Knowledge modeling with Protégé, javaworld, June 2003, http://www.javaworld.com/javaworld/jw-06-2003/
[30]John H. Gennari, Mark A. Musen, Ray W. Fergerson, William E. Grosso, Monica Crubézy, Henrik Eriksson, Natalya F. Noy, and Samson W. Tu, The Evolution of Protégé: An Environment for Knowledge-Based Systems Development, Stanford Medical Informatics, Stanford University, 2002, http://www.smi.stanford.edu/pubs/SMI_Reports/SMI-2002-0943.pdf
[31]Natalya Fridman Noy, Ray W. Fergerson, Mark A. Musen, The knowledge model of Protégé-2000:combining interoperability and flexibility, Stanford Medical Informatics, Stanford University, 2000, http://www.smi.stanford.edu/pubs/SMI_Reports/SMI-2000-0830.pdf
[32]Alexander Maedche, Boris Motik, Ljiljana Stojanovic, Rudi Studer, and Raphael Volz, Ontologies for Enterprise Knowledge Management System, IEEE Computer Society, Computer.org/Intelligent, March/April 2003, http://kaon.semanticweb.org/docus/ieee-is-maedcheetal.pdf
[33]Polanyi,M.(1958), Personal knowledge: Towards a Post-Critical Philosophy, Routledge and Kegan Paul, London
[34]Nonaka, I. and H. Takeuchi(1995), The knowledge creating company,Oxford University
[35]Nonaka, I. (1991), The knowledge creating company, Harvard Business Review, 69, pp.96-104.
[36]Henderson, R. and I. Cockburn (1994), Measuring competence ? Exploring firm effects in pharmaceutical research, Strategic Management Journal, Winter special issue, 15, pp.63-84
[37]Liebeskind, J. P.(1996), Knowledge, strategy, and the theory of the firm, Strategic Management Journal, Winter,special issue, 17, pp.93-107.
[38]Grover, V. and T. Davenport(2001), General perspectives on Knowledge management: Fostering a research agenda, Journal of Management Information Systems,18(1),pp.5-21
[39]Spender, J.–C. (1994), Organizational knowledge, collective practice and Penrose rents, International Business Review, 3(4), pp.353-367
[40]Winter, S. (1988), Knowledge and competence as strategic assets, in D. Teece(ed.), The Competitive Challenge: Strategies for Industrial Innovation and Renewal, Ballinger, Cambridge, MA.
[41]Natalya F. Noy and Deborah L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford Medical Informatics, Stanford University, 2001, http://protege.stanford.edu/publications/ontology_development/ontology101.pdf
[42]Van Heist, G.; Schreiber, T.; Wielinga, B. Using Explicit Ontologies in KBS, International Journal of Human-Computer Studies. Vol. 46. (2/3). 183-292. 1997
[43]Liebowitz, J. & Beckman, T. Knowledge Organizations: What Every Manager Should Know, Boca Raton, FL: St. Lucie Press, 1998, chap5-6
[44]Sheng-Tun Li, An Ontology-based Knowledge Management System for the Metal Industry, 2003, http://www2003.org/cdrom/papers/alternate/P620/p620-li.html
中文部份
[45]何東隆、李美真，「精通XML 與網頁設計」，文魁資訊股份有限公司，Jun，2000。
[46]陳正中、黃中杰、曾順盈，「XML 交戰手冊」，松崗電腦圖書資料股份有限公司，2001。
[47]孟慶凱，「XML 自學方案」，博碩文化，2000。
[48]戴文坡、普賽克原著，胡瑋珊譯，「知識管理：企業組織如何有效運用知識」，台北：中國生產力中心，民88。
[49]陳雪華，「知識組織」，國立台灣大學圖書資訊學系授課教材，91下學期，http://ceiba3.cc.ntu.edu.tw/course/cb9879/，檢索日期：2003/6/1
[50]逢甲大學商學網，http://www.sunny.org.tw/ins/，檢索日期：2003/04/08
[51]聯合理財網保險專區，http://money.udn.com/NASApp/ins/，檢索日期：2003/04/10
[52]劉常勇（民88），「對於知識管理的基本認識」，http://www.cme.org.tw/know/，檢索日期：民91年6月10日
[53]李敘均，「資訊科技與公共組織結構之變革」，私立東海大學公共行政研究所碩士論文，民86。
描述 碩士
國立政治大學
經營管理碩士學程(EMBA)
90932621
91
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0090932621
資料類型 thesis
dc.contributor.advisor 劉文卿zh_TW
dc.contributor.advisor Liou, Wen-chinen_US
dc.contributor.author (Authors) 簡月秀zh_TW
dc.contributor.author (Authors) Chien, Yueh-hsiuen_US
dc.creator (作者) 簡月秀zh_TW
dc.creator (作者) Chien, Yueh-hsiuen_US
dc.date (日期) 2002en_US
dc.date.accessioned 11-Sep-2009 17:59:55 (UTC+8)-
dc.date.available 11-Sep-2009 17:59:55 (UTC+8)-
dc.date.issued (上傳時間) 11-Sep-2009 17:59:55 (UTC+8)-
dc.identifier (Other Identifiers) G0090932621en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/30480-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經營管理碩士學程(EMBA)zh_TW
dc.description (描述) 90932621zh_TW
dc.description (描述) 91zh_TW
dc.description.abstract (摘要) 依財政部保險司每半年對壽險申訴案件的統計觀察，客戶對壽險公司提出申訴的件數有逐年遞增的趨勢，申訴原因多為理賠給付、契約保全服務、業務員招攬、保費收費糾紛、商品條款及法規等爭議。客服員若能於第一時間以專業的知識回應申訴問題，勢必可減少申訴數量。但面對浩瀚的壽險各領域知識，客服中心的成員不易具備所有足以應付客戶問題的知識，實施知識管理將組織內各種知識公開化、標準化、制度化、科技化，已成為刻不容緩的任務。
      為解決壽險客服中心因取得壽險專業知識的障礙而無法提供客戶滿意的服務，本研究對客服中心可能應用的各種知識，探討已被發表的知識管理分類、呈現與檢索等應用的相關技術，提出符合客服中心需求的以知識本體為基礎的壽險知識問題檢索架構，除了提供壽險專業知識的建立、搜尋、分享環境，並將原始文件儲存為XML或RDF的語意文件，做為將來跨平台與跨應用系統資料交換的基礎，提升客服中心服務效率、節省客服人力與行政成本，留住忠誠的客戶以達成CRM的目的，進而提升企業競爭力。		zh_TW
dc.description.abstract (摘要) According to the regular semi-annual statistic report from Insurance Department of MOF Taiwan, the complain cases from policy owners are increasing year by year. The reasons of complain are claims, underwriting, policy services, marketing, premium collections, policy clauses and regulations. If the customer service representatives (CSR) could solve the problems at the right time with their domain knowledge of life insurance, the volumes of complain should be descended explicitly. But it is hard to ask a CSR to play the role with variety domain knowledge of life insurance. Implement the knowledge management mechanism to standardize and enrich the organization memory are the first priority in life insurance companies.
     In order to overcome the barrier of dissatisfaction about customer service, this research try to study the related theories and technologies of knowledge classification, knowledge representation, knowledge acquisition tools, inference and problem solving methods. The results of this research announce an architecture of ontology based customer relationship management for Life Insurance Company to build, represent, search, and share the life insurance domain knowledge. Addition, to save the original semi-structured or unstructured documents into semantic formats of XML or RDF. It could provides the data exchange between heterogeneous databases and platforms. The advantages are more efficiency and cost down for the customer service departments, retain the royalty customers and get more competence advantage for the company.		en_US
dc.description.abstract (摘要) "目 錄
     
     
     第一章 緒論 1
     第一節　研究動機 1
     第二節　研究目的 2
     第三節　預期貢獻 2
     第二章 文獻探討 4
     第一節　知識本體(ONTOLOGY) 4
     2-1-1　知識本體的定義 4
     2-1-2　知識本體的應用 4
     2-1-3　知識本體的種類 6
     2-1-4　知識本體方法論 7
     2-1-5　知識本體語言（Ontology Language） 15
     第二節　語意網（SEMANTIC WEB） 19
     第三節　知識整合工具（KNOWLEDGE ACQUISITION TOOL） 20
     2-3-1　Protégé-2000 20
     2-3-2　KAON語意網架構（The KArlsruhe ONtology and Semantic Web framework） 24
     2-3-3　知識本體為基礎的企業知識管理系統架構 26
     2-3-4　知識本體工具比較 30
     第四節　知識管理 31
     第三章 研究方法與架構 35
     第一節　研究方法 35
     3-1-1　文獻分析法 35
     3-1-2　深度訪談 42
     3-1-3　領域分析 43
     第二節　研究範圍與限制 49
     3-2-1　研究範圍 49
     3-2-2　研究限制 49
     第三節　研究架構 50
     第四章 以知識本體為基礎的壽險客服應用 51
     第一節　壽險客服知識分類 51
     第二節　建立客服知識本體 52
     4-2-1　文件知識本體 52
     4-2-2　壽險領域知識本體 56
     4-2-3　系統架構 57
     4-2-4　系統實作 59
     第五章 結論 70
     第一節　研究成果與貢獻 70
     第二節　未來發展 70
     第六章 參考文獻 72
     英文部份 72
     中文部份 76
     
     
     
     圖　目　錄
     
     圖1-1：人壽保險申訴案件統計 2
     圖2-1：知識本體應用示意圖 5
     圖2-2：知識本體的種類 6
     圖2-3：各知識本體再利用性與實用性比較 7
     圖2-4：Bernaras等Top-level Ontology Methodology 8
     圖2-5：Bernaras等知識本體再利用方法論 9
     圖2-6：SENSUS Ontology Methodology 9
     圖2-7：METHONTOLOGY Framework 10
     圖2-8：Life Cycle of METHONTOLOGY 12
     圖2-9：Conceptualisation of METHONTOLOGY 12
     圖2-10：知識本體導入流程 15
     圖2-11：RDF 基本資料模式 16
     圖2-12：使用RDF圖形描述知識本體 17
     圖2-13：The Semantic Web layers 20
     圖2-14：Protégé-2000發展演進時程 21
     圖2-15：Protégé-2000架構圖 22
     圖2-16：Protégé’s class editor 23
     圖2-17：Protégé’s slots editor 24
     圖2-18：德國Karlsruhe知識本體架構 25
     圖2-19：在Ontology-interface的知識本體演化圖 26
     圖2-20：Ontology-based知識管理系統架構圖 27
     圖2-21：知識本體對應處理流程圖 29
     圖3-1：知識本體發展程序圖 35
     圖3-2：酒與水果概念圖 36
     圖3-3：多重繼承概念圖 37
     圖3-4：酒的相關概念與屬性 38
     圖3-5：子概念與父概念屬性繼承關係 38
     圖3-6：一個以上的父概念 40
     圖3-7：Disjoint Classes 40
     圖3-8：概念循環 41
     圖3-9：Ontology is often just the beginning 42
     圖3-10：個案公司CTI話術畫面樹狀圖 42
     圖3-11：研究架構圖 50
     圖4-1：文件知識本體 52
     圖4-2：上下從屬繼承關係向下查詢 53
     圖4-3：上下繼承關係向上查詢 54
     圖4-4：參照關係 55
     圖4-5：簡單概念與複合概念 56
     圖4-6：壽險知識本體 57
     圖4-7：概念語意查詢系統架構圖 58
     圖4-8：文件知識本體 60
     圖4-9：壽險知識本體 61
     圖4-10：聯繫函屬性值 62
     圖4-11：期刊屬性值 63
     圖4-12：以父層概念屬性條件查詢 64
     圖4-13：所有概念其文件產生者屬性包含『保戶服務部』查詢結果 65
     圖4-14：限定文章概念查詢條件之查詢結果 65
     圖4-15：查詢結果文件詳細內容 66
     圖4-16：聯繫函之XML格式 67
     圖4-17：聯繫函概念與屬性之RDF格式 68
     圖4-18：聯繫函屬性值之RDFs格式 69
     
     
     
     
     
     
     表　目　錄
     
     表2-1：知識本體工具比較 31
     表3-1：商業模式的知識構成要素 45
     表3-2：職業代碼表 46
     表3-3：商學網保險知識分類表 47
     表3-4：聯合理財網保險知識分類表 48
     表4-1：壽險詞彙 57"		-
dc.description.tableofcontents 目 錄
     
     
     第一章 緒論 1
     第一節　研究動機 1
     第二節　研究目的 2
     第三節　預期貢獻 2
     第二章 文獻探討 4
     第一節　知識本體(ONTOLOGY) 4
     2-1-1　知識本體的定義 4
     2-1-2　知識本體的應用 4
     2-1-3　知識本體的種類 6
     2-1-4　知識本體方法論 7
     2-1-5　知識本體語言（Ontology Language） 15
     第二節　語意網（SEMANTIC WEB） 19
     第三節　知識整合工具（KNOWLEDGE ACQUISITION TOOL） 20
     2-3-1　Protégé-2000 20
     2-3-2　KAON語意網架構（The KArlsruhe ONtology and Semantic Web framework） 24
     2-3-3　知識本體為基礎的企業知識管理系統架構 26
     2-3-4　知識本體工具比較 30
     第四節　知識管理 31
     第三章 研究方法與架構 35
     第一節　研究方法 35
     3-1-1　文獻分析法 35
     3-1-2　深度訪談 42
     3-1-3　領域分析 43
     第二節　研究範圍與限制 49
     3-2-1　研究範圍 49
     3-2-2　研究限制 49
     第三節　研究架構 50
     第四章 以知識本體為基礎的壽險客服應用 51
     第一節　壽險客服知識分類 51
     第二節　建立客服知識本體 52
     4-2-1　文件知識本體 52
     4-2-2　壽險領域知識本體 56
     4-2-3　系統架構 57
     4-2-4　系統實作 59
     第五章 結論 70
     第一節　研究成果與貢獻 70
     第二節　未來發展 70
     第六章 參考文獻 72
     英文部份 72
     中文部份 76
     
     
     
     圖　目　錄
     
     圖1-1：人壽保險申訴案件統計 2
     圖2-1：知識本體應用示意圖 5
     圖2-2：知識本體的種類 6
     圖2-3：各知識本體再利用性與實用性比較 7
     圖2-4：Bernaras等Top-level Ontology Methodology 8
     圖2-5：Bernaras等知識本體再利用方法論 9
     圖2-6：SENSUS Ontology Methodology 9
     圖2-7：METHONTOLOGY Framework 10
     圖2-8：Life Cycle of METHONTOLOGY 12
     圖2-9：Conceptualisation of METHONTOLOGY 12
     圖2-10：知識本體導入流程 15
     圖2-11：RDF 基本資料模式 16
     圖2-12：使用RDF圖形描述知識本體 17
     圖2-13：The Semantic Web layers 20
     圖2-14：Protégé-2000發展演進時程 21
     圖2-15：Protégé-2000架構圖 22
     圖2-16：Protégé’s class editor 23
     圖2-17：Protégé’s slots editor 24
     圖2-18：德國Karlsruhe知識本體架構 25
     圖2-19：在Ontology-interface的知識本體演化圖 26
     圖2-20：Ontology-based知識管理系統架構圖 27
     圖2-21：知識本體對應處理流程圖 29
     圖3-1：知識本體發展程序圖 35
     圖3-2：酒與水果概念圖 36
     圖3-3：多重繼承概念圖 37
     圖3-4：酒的相關概念與屬性 38
     圖3-5：子概念與父概念屬性繼承關係 38
     圖3-6：一個以上的父概念 40
     圖3-7：Disjoint Classes 40
     圖3-8：概念循環 41
     圖3-9：Ontology is often just the beginning 42
     圖3-10：個案公司CTI話術畫面樹狀圖 42
     圖3-11：研究架構圖 50
     圖4-1：文件知識本體 52
     圖4-2：上下從屬繼承關係向下查詢 53
     圖4-3：上下繼承關係向上查詢 54
     圖4-4：參照關係 55
     圖4-5：簡單概念與複合概念 56
     圖4-6：壽險知識本體 57
     圖4-7：概念語意查詢系統架構圖 58
     圖4-8：文件知識本體 60
     圖4-9：壽險知識本體 61
     圖4-10：聯繫函屬性值 62
     圖4-11：期刊屬性值 63
     圖4-12：以父層概念屬性條件查詢 64
     圖4-13：所有概念其文件產生者屬性包含『保戶服務部』查詢結果 65
     圖4-14：限定文章概念查詢條件之查詢結果 65
     圖4-15：查詢結果文件詳細內容 66
     圖4-16：聯繫函之XML格式 67
     圖4-17：聯繫函概念與屬性之RDF格式 68
     圖4-18：聯繫函屬性值之RDFs格式 69
     
     
     
     
     
     
     表　目　錄
     
     表2-1：知識本體工具比較 31
     表3-1：商業模式的知識構成要素 45
     表3-2：職業代碼表 46
     表3-3：商學網保險知識分類表 47
     表3-4：聯合理財網保險知識分類表 48
     表4-1：壽險詞彙 57		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0090932621en_US
dc.subject (關鍵詞) 知識本體zh_TW
dc.subject (關鍵詞) 知識整合工具zh_TW
dc.subject (關鍵詞) 人壽保險zh_TW
dc.subject (關鍵詞) 客服中心zh_TW
dc.subject (關鍵詞) Ontologyen_US
dc.subject (關鍵詞) Knowledge Acquisitionen_US
dc.subject (關鍵詞) Protege2000en_US
dc.subject (關鍵詞) KAONen_US
dc.title (題名) 以知識本體為基礎的壽險客服應用zh_TW
dc.title (題名) Ontology Based CRM for Life Insurance Companyen_US
dc.type (資料類型) thesisen
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