1. Final Words Jian-hua Yeh ( 葉建華 ) 真理大學資訊科學系助理教授 au4290@email.au.edu.tw

2. 2 Outline What have we learned so far? What could a digital library really do? More selected extensions Final words

3. 3 What have we learned so far? Digital content industry overview Digital library case studies Digital repository system Digital library related technologies Knowledge management issues

4. 4 Digital Content Industry Overview Government project: e-Taiwan NSC-DMP NSC-NDAP CCA-NRCH

5. 5

6. 6 NSC Projects NSC Digital Museum Project (DMP) –1998 – 2002 –Pilot projects & digital museum projects NSC National Digital Archives Program (NDAP) –2002 -- 2006

7. 7 NSC DMP 主題計畫 語文藝術類﹕ 4 件 人文社會類﹕ 12 件 自然生態類﹕ 5 件 生活醫療類： 4 件 建築與地理類﹕ 3 件 技術支援計畫 人文與自然資源地圖 搜文解字 ─ 語文知識網路 資源組織與檢索之規範 系統評估 數位典藏系統先導計畫 數位博物館影像版權資訊植入技術與 軟體之開發

8. 8 NSC NDAP Combination of three major NSC projects –Digital museum project –Digital archive project –International digital library project with NSF (US) As a basis of e-Taiwan project

9. 9 NSC NDAP (cont.)

10. 10 CCA NRCH 文化藝術活動資訊網路 提昇政府文化服務 及國際行銷 數位文化加值計畫 故宮文物數位博物館建置 與加值應用計畫 創造文化產業經濟 加強文化藝術資源數位化 與應用 國家文化資料庫建置計畫 國家文化藝術人才庫建置計畫 文化藝術主題知識庫 文化藝術數位資源應用與呈現計畫 強化文化機構基礎建設 ‧ 文化藝術機構基礎建設

11. 11 Digital Archive Applications 原有典藏 數位化檔案群 文化產業 加值產業 內容產業 軟體產業 所有資訊相關之產業 教育與學習 研究與發展 資訊共享、公共資訊系統 創造力、生產力、競爭力 以及生活品質的提升 政府各部會 民眾

12. 12 Digital Library Case Studies NTU digital library/museum project –Content generation: centralized –Information management: centralized –Information access: centralized NSC NDAP project –Content generation: distributed –Information management: distributed –Information access: distributed CCA NRCH project –Content generation: distributed –Information management: centralized & distributed –Information access: centralized

13. 13 Usage Model Centralized information access –Centralized information-lookup only Catalog (meta-information) centralized NDAP union catalog project: OAI-based –Centralized information-lookup & content access Catalog & content centralized NRCH digital archive project Distributed information access –Distributed information lookup OpenURL, Z39.50 –Distributed content access DOI

14. 14 Related Technologies XML/DTD Metadata description Multimedia processing You are already familiar with this from your term project!

15. 15 Knowledge Management Issue Turning data into information –Resource organization convert digital content into useful information Meta-information Turning information into knowledge –Information organization Semantics generation: ontology creation Meta-meta-information Classification problem

16. 16 The Problem With the increasing complexity of our systems and our IT needs, we need to go to human level interaction We need to maximize the amount of Semantics we can utilize From data and information level, we need to go to human semantic level interaction DATAInformationKnowledge Run84 ID=08 NULL PARRT ACC ID=34 e 5 & # ~ Q ü @ ¥ ¥ Æ � Å Tank ¥ Noise Human Meaning Vehicle Located at Semi-mountainous terrain obscured decide Vise maneuver And represented semantics means multiple represented semantics, requiring semantic integration

17. 17 Interpretation Continuum Simple Metadata: XML Human interpreted Computer interpreted DATA KNOWLEDGE Relatively unstructured Random Very structured Logical Moving to the right depends on increasing automated semantic interpretation Info retrieval Web search Text summarization Content extraction Topic maps Reasoning services Ontology Induction... Display raw documents; All interpretation done by humans Find and correlate patterns in raw docs; display matches only Store and connect patterns via conceptual model (i.e,. an ontology); link to docs to aid retrieval Automatically acquire concepts; evolve ontologies into domain theories; link to institution repositories (e.g., MII) Richer Metadata: RDF/S Very Rich Metadata: DAML+OIL Automatically span domain theories and institution repositories; inter- operate with fully interpreting computer Interpretation Continuum

18. 18 Complexity of Ontology

19. 19 OWL: Web Ontology Language

20. 20 XTM: Topic Maps Language

21. 21 Semantic Analysis

22. 22 Categorization & Visualization

23. 23 Key Persons Information level –Content experts –Computer technologists –Library/Information experts Knowledge level –Content experts –Computer technologists –Cognitive scientists

24. 24 What could a digital library really do? Preservation Education Research Development –Application –Innovation

25. 25 Selected Digital Library Extensions Presentation –RIA: rich internet application Flash-based presentation AJAX-based presentation AFlax: combining Flash and AJAX technologies –Visualization Service –Web service application UDDI –Knowledge service Standard transformation: XTM, OWL, SKOS, etc. Extension –Education: SCORM Archive/library content to learning content

26. 26 AJAX

27. 27 Conclusion