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Gleaning Information From Embedded Metadata

Put GRDDL-enabled agents to the task of extracting valuable information from machine-processable metadata embedded in documents, courtesy of prevailing semantic web standards. 


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ne of the fundamental visions of the semantic web is the ability to provide improved technologies for machine-processable data. The current web is a swell place for people, but absent a series of open, global standards for metadata, it is difficult to imagine the interoperability necessary to link software, data, and documents in all their various forms. (Note that only the standards are required to be global; the specific terms, relationships, and concepts can be as diverse as the communities they reflect.)

While the vision eventually spirals off into spiders, agents, and bots, you do not have to go quite that far to imagine how vitally useful this automated data processability will be. Right now, the only real metadata available everywhere is the address of the documents you browse and the date and time when you did so. You can collect this data in your browser or on sites like del.icio.us to find them at a future point through tags that you create. You are externalizing the metadata about the document either into a taxonomy (that is, browser history menus) or through keyword tags. It is the browsing experience that directly provides the where and when.


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