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Final Words

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. Outline. What have we learned so far? What could a digital library really do? More selected extensions Final words. What have we learned so far?. Digital content industry overview Digital library case studies

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Final Words

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

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

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

  6. NSC DMP 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 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

  7. 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

  8. NSC NDAP (cont.)

  9. CCA NRCH 創造文化產業經濟 • 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 提昇政府文化服務 及國際行銷 加強文化藝術資源數位化 與應用 • 文化藝術活動資訊網路 • 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

  10. Digital Archive Applications 原有典藏 數位化檔案群 民眾 政府各部會 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

  11. 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

  12. 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

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

  14. 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

  15. DATA Information Knowledge decide ID=34 Vehicle Located at Vise maneuver ACC ID=08 Tank NULL obscured PARRT Semi-mountainous terrain ¥ Run84 Noise Human Meaning ¥ Å @ ü Q # ¥ e & 5 ~ Æ � 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 semanticlevel interaction • And represented semantics means multiple represented semantics, requiring semantic integration

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

  17. Complexity of Ontology

  18. OWL: Web Ontology Language

  19. XTM: Topic Maps Language

  20. Semantic Analysis

  21. Categorization & Visualization

  22. Key Persons • Information level • Content experts • Computer technologists • Library/Information experts • Knowledge level • Content experts • Computer technologists • Cognitive scientists

  23. What could a digital library really do? • Preservation • Education • Research • Development • Application • Innovation

  24. 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

  25. AJAX

  26. Conclusion

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