ImageSnippets - Using Linked Data Metadata to Organize, Share and Publish your Images


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ImageSnippets is a general purpose product for managing your images that uses linked data metadata for image description. Keywords become more meaningful, searching for images is enriched. More accurate descriptions can be gathered by experts across your global teams. Images can be published with your publishing intentions remaining clearly associated with your images.

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ImageSnippets - Using Linked Data Metadata to Organize, Share and Publish your Images

  1. 1. ImageSnippets™ is a new approach to the management of images (and other digital resources) using semantic technology in the form of linked data. Semantically aware applications can take advantage of both traditional and RDFa metadata distributed with the images. <span rel="rdfs:comment"><span property="rdfs:label" content="A sign alongside the abandoned tracks of the Gulf Mobile and Ohio railroad. A jet aircraft flies overhead."></span></span></span><span rel="lio:hasSetting"><span source="[dbpedia:Kentucky]">Kentucky</span></span></span><spanabout"><span rel="lio:hasInBackground"> <span typeof="dbpedia:Contrail">acontrail</span> </span></span>
  2. 2. Tagging with linked data offers improved tag management, querying and innovative methods for the transport and re-use of an image with it's data.
  3. 3. ImageSnippets Users: • Manage collections of resources (images, video, documents) and they need to share or publish those resources, often in multiple places simultaneously and want to retain control of all of their data. • Want to describe their images with much greater depth and clarity (disambiguation and contextual tagging) and ensure that these descriptions continue to persist with the image no matter where it gets shared or posted.
  4. 4. images with essentially the same keywords can easily add up to hundreds of thousands
  5. 5. keywords need context and disambiguation the fit of the hood in red/green primer of car 130985, that shows the gap between the hood and the body and shows a chalk line on the cowl cowl: The hood or hooded robe worn especially by a monk. b. A draped neckline on a woman's garment. 2. A hood-shaped covering used to increase the draft of a chimney. 3. The top portion of the front part of an automobile body, supporting the windshield and dashboard. 4. The cowling on an aircraft.
  6. 6. In ImageSnippets, users can layer metadata as additional knowledge about the images reveals itself in various contexts. Data can be added without having to re-write user vocabularies, re-share or re-post the images. All metadata added to images in ImageSnippets is dynamically available through shared or embedded links to files. So perhaps one person in a team might identify superficial data (it's a crab or jellyfish). and then other specialists might identify even more specific features– all on the same images in the same system – searchable and re-usable by all. and later, a crustacean biologist subject matter expert - located around the world can identify the species
  7. 7. ImageSnippets automatically provides common datasets such as: dbPedia, Yago, Freebase. But users can also define their own entities and datasets to describe their own particular subject domain. Previously engineered datasets can be loaded into ImageSnippets or the datasets can evolve as part of the curation process. The creation and evolution of dataset terms can be orchestrated by an administrator exclusively or with collaborative input from a team of users.
  8. 8. Resources managed in ImageSnippets are copy written in a way that is not easily stripped from the image, thereby reducing the likelihood that shared or posted images will be classified as 'orphan works‘. A link to this image looks like: •The link displays this image in the browser window. •The image contains standard IPTC and XMP data in it’s header. •A link to the HMTL file itself is embedded in the XMP and can be followed by a semantically aware application. •The file contains all descriptive metadata, copyright and contact information written in industry standard RDFa. which means you can share your link here: and worry less about your data disappearing The first of two spans of the Sunshine Skyway bridge, built in 1954 and connecting Bradenton and Saint Petersburg, Florida. This bridge fell in 1980, when it was hit by a barge. © 1954 – 2013 Bob Preston Images
  9. 9. How it works: Semantic technology links data using RDF: a subject, a property and an object The subject of the image can either be the image or a region in the image. The property describes how the keyword relates to the image, such as: "depicts" or “shows". The object is like a normal keyword phrase or tag, such as: "Burt Reynolds" or "Björn Waldegärd. RDF (Resource Description Framework) is a language for describing data about resources it’s construction uses URI’s (universal resource identifiers (i.e. web addresses) http://imagesnippets/thisImage.jpg http://lio:depicts http://dbpedia:Burt_Reynolds
  10. 10. Google (and other search engines) read and use semantic information found with resources Rich Snippets
  11. 11. ImageSnippets has built in properties for giving context to keywords But you can also use properties from: other sources such as or design your own
  12. 12. ImageSnippets has an internal search function that sorts results by property, a search for ‘New Orleans, Louisiana’, for example, might return: Advanced users can write their own SPARQL queries against the triple stores and named graphs using our own endpoint.
  13. 13. Ontologies link related information – so searches can also return results without exact text based matches: The returned results from this example found bird images even though the text string ‘bird’ was not used anywhere in the image description
  14. 14. ImageSnippets has many more features and uses. We invite you to take a closer look at: . © 2013 Metadata Authoring Systems, LLC