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The Economy of Distributed Metadata Authoring

The Economy of Distributed Metadata Authoring. by Stefano Mazzocchi. Experts' Workshop - Perspectives on networked knowledge spaces 25/26 October 2002, Sankt Augustin, Germany Organised by: MARS Exploratory Media Lab at the Fraunhofer Institut für Medienkommunikation. What is Metadata.

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The Economy of Distributed Metadata Authoring

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  1. The Economy of Distributed Metadata Authoring by Stefano Mazzocchi Experts' Workshop - Perspectives on networked knowledge spaces 25/26 October 2002, Sankt Augustin, Germany Organised by: MARS Exploratory Media Lab at the Fraunhofer Institut für Medienkommunikation

  2. What is Metadata • Metadata is information about information

  3. Classic Examples of Metadata • Keywords • Author • Date of creation/modification • Address/Identifier

  4. More provocative examples • Punctuation in text • Layout on a page • Font size/weight/style in text • Commentary audio tracks on DVD

  5. General Metadata Properties • Metadata is data about data, but it’s still data • Metadata should be semantically orthogonal: data should be understandable even without metadata

  6. Markup and Metadata • Markup languages can be seen as metadata-driven languages. • Markup syntax is designed to keep data and metadata orthogonal

  7. The Importance of Metadata • Key to semantic analysis • Key to multidimensional augmentation of information • Key to information relationability • In short: key to more powerful datamining

  8. Types of Metadata • Human authored • Automatically Inferred

  9. Human Authoring (1) • In-process: data and metadata are created at the same time • Out-of-process: metadata is added after data has been created

  10. Human Authoring (2) • By the data author: data and metadata are written by the same person • By another author: data and metadata are created by different people

  11. Automatic Inference • Recogniction of patterns and trends in data • Semantic assumption of data-metadata correlations

  12. Types of Automatic Inference • Heuristic: some algorithm performs analysis on the data set (artificial reproduction of intelligent behavior) • Transparent: some mechanically extracted information is transparently associated with some metadata performed by human semantic analysis

  13. Transparent Inference Examples • Google’s PageRank • Amazon’s related items • NEC’s CiteSeer

  14. Google • PageRank is the system that ranks the pages found after a query against their database • It works on hyperlink topology analysis • Metadata is inferred from the hyperlinks contained into the page

  15. Amazon • Relation between items is inferred from the analysis of the articles bought by the other users • The act of a user buying two products is assumed to be a sign of relation between the items • Simply by buying, the users are collectively filling up product metadata on relations

  16. CiteSeer • Digital Library of IT papers • Ranks searches on ‘citations’ topology analysis • Bibliographies become the source of relevance metadata

  17. The Issues with Metadata • Quality of metadata heavily influences the quality of all search/retrieval systems

  18. First Law of Metadata Quality • Artificial intelligence is just that: artificial! • So: for a system that feels smart to humans, you need human-created metadata

  19. First Law of Metadata Quantity • The more high-quality metadata, the better. • But: the more human-created metadata, the more expensive the authoring process gets.

  20. Metrics • In order to estimate the value of proposed technological solutions, a metric is required • Economical feasibility is one possible metric

  21. Consequences • All current markup-based semantic web solutions (RDF, topic maps, ontologies) are economically infeasible. • The best semantic solutions are those based on transparent inference

  22. Suggestions (1) • Plan the impact of metadata authoring costs on technology decisions. • Don’t underestimate the importance of user feeling. • Think about what can be inferred transparently without requiring heuristics

  23. Suggestions (2) • Do all efforts to make instant return on the investment of metadata authoring • Don’t ask too much • Be smart but not smarter

  24. Thanks!

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