1. Automatic Metadata Generation through Extraction LIS 688 / spring 2011 Group 1 MendyOzan, EunaeChae, and Vanessa Smith 1
2. Overview of automatic metadata generation Differences between harvesting and extraction Examination of extraction (6 literature reviews) Examination of Klarity Conclusion Outline 2
3. With increased wed-based information resources Limited resources – labor and money “ For libraries to advance and take leadership in the bibliographic control of web resources, they must investigate more efficient and less costly metadata creation methods.” – Greenberg, Spirgin, and Crystal, 2005 3 Background
4. Help serve the need for more efficient and less costly metadata creation Increase consistency in records : more searchable and interoperable Cut down on the amount of human labor 4 Benefits
5. Harvesting : rely on machine-enabled collection of previously tagged or populated metadata fields Extraction : use complex indexing algorithms and mining techniques to read the contents of a resource, analyze this content, and extract information for application to a metadata schema Main difference : the type of content that is read and analyzed by the program 5 Main methods
6. Rule-based systems based on natural language processing : used to extract metadata from educational materials Machine learning methods : used to extract titles from general documents Weaknesses : many methods mainly extract metadata from the first page of a document but not from the inner pages of a document 6 More about Extraction
7. Automatic Metadata Retrieval form Ancient Manuscripts by Le bourgeosis, F.& Kalieh, H. (2004) Aimed at processing automatically digitized manuscripts by using a generic platform which can be used by non-specialists in image processing and pattern recognition Finding : the source of the image was the main factor in deciding if the automatically generated metadata was good : the quality of image is the key. 7 Literature reviews (1)
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9. Generate metadata five elements : identifier, title, concepts, keywords, and descriptions
10. Some elements (Identifier and title) are harvested directly form the resources while keywords, description, and concept are generated through extraction.8 Literature reviews (2)
11. Klarity provides an interface for manual editing and addition to the metadata Weakness of description: a smaller amount of text creates better metadata Inaccuracy of Title and keywords - character limit in the title field (>100) But it has potential as it is further developed 9 Literature reviews (2)
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13. DC.dot finds the resource “identifier” from the web browser’s address prompt and pulls the remaining resources from the source code metadata 10 Literature reviews (2)
14. Still Require human oversight and input Scored low on their accuracy evaluations (86.2% for Klarity, 78.6% for DC.dot) 11 Literature reviews (2)
15. New possibilities for metadata creation in an institutional repository context Institutional Repository : archive, distribute, manage, and preserve research efforts Focused on the use of two methods of metadata : text mining and machine learning 12 Literature reviews (3)
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18. Number of characters, nouns, verbs per line, adjectives per line can all signal the system to identify specific elements 14 Literature reviews (3)
19. Findings : records with good metadata can be taught to the system to those records other records could have similar metadata created 15 Literature reviews (3)
20. Automation generation and extraction Provide overview of metadata, and automatic generation More detailed about DescribeThis Using the Dublin Core format, it will create records that web administrators can use in describing their own web content 16 Literature reviews (4)
21. Automated document metadata extraction Focused on theses and dissertations Nature of these documents : standard headers, titles, table of contents, abstract, acknowledgement, preface, introduction, conclusion and references There preexisting categories aid in the automatic generation Rule- based system, extract more metadata 17 Literature reviews (5)
22. Application of semi-automatic metadata generation in libraries Metadata creation through partial reliance on software in combination with human process Findings : the application has not yet been fully exploited, still exist the gap between experimental studies and the usage in a real world setting More research needed on the development of automatic metadata generation in practical setting 18 Literature reviews (6)
23. The role of automatic generation is critical along with the enormous volume of online and digital resources Hold great promise for increasing the efficiency and consistency of metadata generation But many metadata extraction programs are limited and are not perfect in using it in real setting The key to solve the problem : increased standardization in format and schemas 19 Conclusion
24. Le Bourgeosis, F., & Kaileh, H. (2004). Automatic Metadata Retrieval from Ancient Manuscripts. In A. e. Dengel, Document Analysis Systems VI. Berlin: Springer Berlin / Heidelberg. Greenberg, J. (2004). Metadata Extraction and Harvesting: A Comparison of Two Automatic Metadata Generation Applications. Journal of Internet Cataloging, 6 (4), 59-82. Al-Digeil, M., Burk, A., Forest, D., & Whitney, J. (2007). New possibilities for metadat creation in an institutional repository context. OCLC Systems & Services, International Digital Library Perspectives, 23 (4), 403-410 20 Reference
25. Noufal, P. (2005). Automatic generation and extraction. In: 7th MANLIBNET Annual National Convention on Digital Libraries in Knowledge Management: Op- portunities for Management Libraries, Indian Institute of Management Kozhikode (pp. 319-327). Kolkata: IIM Kolkata. Ojokoh, B. A., Adewale, O. S., & Falaki, S. O. (2009). Automated document metadata extraction. Journal of Information Science , 563-570. Park, J.-r., & Lu, C. (2009). Application of semi-automatic metadata generation in libraries: Types, tools and techniques. Library & Information Science Research , 225-231. 21 Reference
