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Reliable and Persistent Identification of Linked Data Elements

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Abstract

Linked Data techniques rely upon common terminology in a manner similar to a relational database’vs reliance on a schema. Linked Data terminology anchors metadata descriptions and facilitates navigation of information. Common vocabularies ease the human, social tasks of understanding datasets sufficiently to construct queries and help to relate otherwise disparate datasets. Vocabulary terms must, when using the Resource Description Framework, be grounded in URIs. A current bestpractice on the World Wide Web is to serve vocabulary terms as Uniform Resource Locators (URLs) and present both human-readable and machine-readable representations to the public. Linked Data terminology published to theWorldWideWeb may be used by others without reference or notification to the publishing party. That presents a problem: Vocabulary publishers take on an implicit responsibility to maintain and publish their terms via the URLs originally assigned, regardless of the inconvenience such a responsibility may cause. Over the course of years, people change jobs, publishing organizations change Internet domain names, computers change IP addresses,systems administrators publish old material in new ways. Clearly, a mechanism is required to manageWeb-based vocabularies over a long term. This chapter places Linked Data vocabularies in context with the wider concepts of metadata in general and specifically metadata on the Web. Persistent identifier mechanisms are reviewed, with a particular emphasis on Persistent URLs, or PURLs. PURLs and PURL services are discussed in the context of Linked Data. Finally, historic weaknesses of PURLs are resolved by the introduction of a federation of PURL services to address needs specific to Linked Data.

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    David Wood

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Wood, D. (2010). Reliable and Persistent Identification of Linked Data Elements. In: Wood, D. (eds) Linking Enterprise Data. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7665-9_8

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