dhwi linked open data - show and tell
DESCRIPTION
Class Show and Tell from "Publishing and Using Linked Open Data" at the University of Maryland's Digital Humanities Winter Institute.TRANSCRIPT
What is Linked Open Data? Data published by exis0ng internet protocols that use a URI (Unique Resource Indicator) as the primary discoverable en0ty for a resource (e.g. person, object, web page, etc.) THE FIVE STARS OF LOD: ★ make your stuff available on the Web (whatever format) under an open license ★★ make it available as structured data (e.g., Excel instead of image scan of a table) ★★★ use non-‐proprietary formats (e.g., CSV instead of Excel) ★★★★ use URIs to iden0fy things, so that people can point at your stuff ★★★★★ link your data to other data to provide context
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What is it good for (HUH!)? Making your data more discoverable and useful by everybody Making the web machine-‐readable at a more granular level Allowing for more sophis0cated queries using inference Connec0ng your data to other people’s data Examples: hPp://exhibi0ons.europeana.eu/, hPp://pelagios-‐project.blogspot.com/
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Modeling and Expressing Using established and/or custom ontologies Using RDF (Resource Descrip0on Framework) RDF Triples:
For example: A URI that represents the person Georgina has the name Georgina Goodlander <hPp://countedshadows.com/wordpress#> foaf:hasName “Georgina Goodlander” The person Georgina in this URI Is the same thing as The person Georgina in this URI <hPp://countedshadows.com/wordpress#> skos:exactMatch <hPp://dbpedia.org/resource/Goodlander_G>
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Prepping Data
hPps://github.com/OpenRefine
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Steps to PublicaKon Export data as RDF Publish on the web Celebrate!
hPp://linkeddatabook.com/edi0ons/1.0/#htoc61
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Resources & Free Stuff to Use! Class Syllabus & Wiki: hPp://lod4h.pbworks.com #lod4h Open Refine for cleaning data and assigning RDF proper0es: hPps://github.com/OpenRefine Protégé for crea0ng and edi0ng ontologies: hPps://github.com/OpenRefine Data Hub for finding other open data sets: hPp://datahub.io/
Challenges? Few exis0ng examples of applica0on Limita0ons of established ontologies Conceptualizing messy data is hard Daun0ng scale Developing and maintaining automa0on and workflow Time and resources
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