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Publishing Linked Data using


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An introduction to Linked Data publishing, starting from the "Why?" and giving the outline of the "How?"

An introduction to Linked Data publishing, starting from the "Why?" and giving the outline of the "How?"

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  • 1. Publishing Linked Data using Development and management of e-Repositories – OTA IODE, Oostende, Belgium, April 11th, 2013 An introduction to the project of Mr. Aditya Kakodkar
  • 2. LinkedData, Why?● External/Internal (Reference) Data use and reuse● (Meta) Data encoded and published along standardized, perennial and documented measurement systems and categories● Massive international efforts for tools and interlinked repositories development● Opportunity to become a General Reference on the Web for a specific domain● Your work becomes discoverable and well positioned by Search Engines
  • 3. Data to be linked ?● Metadata provides the context, links to a MODEL● Observed Data: source, measure/range, unit...● Manually entered Data: validation rules● Aggregated Data: Which indicator for which decision?● Published Data: exact? complete? perenial?● Reference Data: comparability with other data?● Open Data is (not) Public Data!● Personal Data: protection? anonymisation?● Big Data: dangers? opportunities?
  • 4. Linking Data in order to...● Denote an “real life” object, a concept, a transaction... – not uniquely enough:● Document (explain, contextualize) the data to the user (HTML document page)● Enrich, linking to other data ... (RDF data page)
  • 5. LinkedData semiotic triangle
  • 6. RDF: Resource Description Framework● A standard to provide (meta)data on the Web● Based on a very simple model of triplets: subject – property – object● Everything is an URI; object can also be a “constant value” (a text, a number, a date...) suffixed by an indication of the language● Example: dbpedia:European_Herring_Gull rdfs:label “Goéland argenté”@fr where “dbpedia:” stands for URI prefix: and “rdfs:” stands for URI prefix:
  • 7. Being a Gull is not Dull !●● which redirects to the document (HTML for human consumption):● Data (for machine consumption) is generated separately in different formats (N3, Turtle, XML, JSON...) :● Browser negotiates the suitable format...● What is validated there? What are the rules?● Can it be a reference to take decisions?
  • 8. Using a single page?● RDFa and MicroData are two standards to MERGE an HTML document (made for humans) and the data a machine may wish to extract from it● Example from a page in <h1>Details of<span itemprop="name"> <span itemprop="familyName">Dupriez</span> ,&nbsp; <span itemprop="givenName">Christophe </span> </span></h1>●, an Open Source software to collect data embedded in a Web Page will be demonstrated later on
  • 9. Data Model● Which processes do we need to automate? (use cases)● Which entities (real objects, concepts, transactions/events) have to be represented?● How do those entities interrelate?● What measures (properties) are made about each type of entity?● Reuse: who else will align on the same model? What Google may do with my data?
  • 10.● is a modelling initiative of Google / Microsoft / Yahoo to standardize URIs for RDF properties● Common model for data published as documents harvestable on the web● Their goal is to collect the data in our pages. Those pages are then better indexed. What else? (A.I.?)● models are far from exhaustive (for instance, insufficient for CVs) but a “/extension” mechanism exists● Examples on the site
  • 11. Google RichSnippets● Google Spider extracts data tagged using RDFa or MicroData● Pages with such data are promoted...● Google Search Engine enriches results using this data● Example “Apollo Theatre”: place, events, reviews...● Google RichSnippets tool validates a web page:
  • 12. Data Search Engine● ANY23 is used to feed SINDICE, the Search Engine for RDF data● Example: