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FEDERE ARAMA KAVRAMI

This presentation discusses the techniques and challenges associated with federated search across diverse metadata applications and document types. It highlights the Harvest API and OAI extensions for managing metadata complexity, enabling effective data mining and federated search across various data repositories. Key features include selective harvesting of OAI-compliant data, exposing combined metadata sets, and employing the Self-Organizing Map algorithm for improved search effectiveness. Presented at the IMA Workshop in April 2004, it aims to enhance knowledge discovery in digital libraries.

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FEDERE ARAMA KAVRAMI

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  1. FEDERE ARAMA KAVRAMI DOK 422 Bilgi Ağları (Bahar 2006) Yaşar Tonta H.Ü. BBY 3.-14. slaytlar için kaynak: Su-Shing Chen, Indexing Mathematical Abstracts by Metadata and OntologyIMA Workshop, April 26-27, 2004,http://www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  2. Federe Arama • Farklı metadata uygulamaları, derme geliştirme politikaları, belge türleri, erişim algoritmaları vs. olan veri tabanları/havuzları üzerinde ortak arama yapılması

  3. Harvest API Harvester OAI_DC Data Mining Data Provider Service Provider DL Server Data Provider Service Provider OAI_XXX Federated Search A DL Server with OAI Extensions: Managing the Metadata Complexity Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  4. Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  5. A DL Server with OAI Extensions: Managing the Metadata Complexity Built in capabilities: • Harvester – harvest various OAI compliant data providers • Data provider – expose harvested and existing metadata sets • Service provider – federated search and data mining capabilities on metadata sets Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  6. Harvest API Data Providers • Harvester Interface: • URL to harvest • Selective harvesting • parameters harvest Harvester parameters harvest Harvested metadata … DL Server Harvester Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  7. Harvester Interface Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  8. Harvester Interface Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  9. Data Provider • Expose single or combined metadata sets harvested to other harvesters • Reformat metadata from different data providers to be harvested by other service providers (e.g., originally Dublin Core, reformat to MARC before exposing) Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  10. Service Provider: Federated Search • Emulating a federated search service on existing and combined harvested metadata sets • Federated search across potentially other search protocols Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  11. Federated Search Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  12. Federated Search Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  13. Federated Search Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  14. Service Provider: Data Mining • Knowledge discovery on harvested metadata sets • Metadata classification using the Self-Organizing Map (SOM) algorithm • Improving retrieval effectiveness by providing concept browsing and search services Kaynak: www.ima.umn.edu/talks/workshops/ 4-26-27.2004/chen/chen.ppt

  15. Henüz sonuç yok.

  16. NDLTD

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