Abstract
This paper describes our efforts to develop a toolset and process for automated metadata extraction from large, diverse, and evolving document collections. A number of federal agencies, universities, laboratories, and companies are placing their collections online and making them searchable via metadata fields such as author, title, and publishing organization. Manually creating metadata for a large collection is an extremely time-consuming task, but is difficult to automate, particularly for collections consisting of documents with diverse layout and structure. Our automated process enables many more documents to be available online than would otherwise have been possible due to time and cost constraints. We describe our architecture and implementation and illustrate the effectiveness of the tool-set by providing experimental results on two major collections DTIC (Defense Technical Information Center) and NASA (National Aeronautics and Space Administration).
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Flynn, P., Zhou, L., Maly, K., Zeil, S., Zubair, M. (2007). Automated Template-Based Metadata Extraction Architecture. In: Goh, D.HL., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds) Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers. ICADL 2007. Lecture Notes in Computer Science, vol 4822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77094-7_42
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DOI: https://doi.org/10.1007/978-3-540-77094-7_42
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