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The Web of Data emerging industries

The Web of Data emerging industries . Michalis Vafopoulos 04/04/2013. Contents . The Web of documents vs. Web of data Some technology Some economics ..and action PSGR project and more…. The Web of Documents. Simple, big and unstructured Organized in Silos But humans:

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The Web of Data emerging industries

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  1. The Web of Data emerging industries Michalis Vafopoulos 04/04/2013

  2. Contents • The Web of documents vs. Web of data • Some technology • Some economics • ..and action • PSGR project • and more…

  3. The Web of Documents • Simple, big and unstructured • Organized in Silos But humans: • are interested in Things, no documents & these Things might be in docs or elsewhere • Limited capacity to extract meaning...

  4. The Web of Data • Analogy:a global file system ----> globaldatabase • Designed for: human consumption ->machines first, humans later • Primary objects: documents --> things (or descriptions of things) • Links between: documents--> things • Degree of structure in objects: fairly low ---> high • Semantics of content and links: implicit --> explicit (Tom Heath)

  5. The Web of Data: why? • encourages reuse • reduces redundancy • maximizes its (real and potential) inter-connectedness • enables network effects to add value to data

  6. The Web of Data: how? • – current state on the Web • Relational Databases • APIs • XML • CSV • XLS • Computers can’t consume data because: • Different formats & models • Not inter-connected

  7. The Web of Data: how? – we need to create a standard way of publishing Data on the Web (like HTML for docs) This is the Resource Description Framework (RDF) (a simple example here from Juan F. Sequeda), more next semester!)

  8. Resource Description Framework (RDF) • A data model • A way to model data • Inspired form Relational databases and Logic • RDF is a triple data model • Labeled Graph (semantic networks) • Subject, Predicate, Object <Isidoro> <was born in> <Chios> <Chios> <is part of> <Greece>

  9. Example: Document on the Web

  10. Databases back up documents THINGS have PROPERTIES: A Book as a Title, an author, … This is a THING: A book title “Programming the Semantic Web” by Toby Segaran, …

  11. Data representation in RDF Programming the Semantic Web title author book Toby Segaran isbn 978-0-596-15381-6 publisher name Publisher O’Reilly

  12. Everything on the web is identified by a URI!

  13. link the data to other data Programming the Semantic Web title author http://…/isbn978 Toby Segaran isbn 978-0-596-15381-6 publisher name http://…/publisher1 O’Reilly

  14. consider the data from Revyu.com hasReview http://…/review1 http://…/isbn978 description reviewer Awesome Book http://…/reviewer name Juan Sequeda

  15. start to link data hasReview http://…/review1 http://…/isbn978 Programming the Semantic Web description title hasReviewer sameAs Awesome Book author http://…/isbn978 Toby Segaran http://…/reviewer name isbn 978-0-596-15381-6 Juan Sequeda publisher name http://…/publisher1 O’Reilly

  16. Juan Sequeda publishes data too http://juansequeda.com/id http://dbpedia.org/Austin livesIn name Juan Sequeda

  17. Let’s link more data hasReview http://…/review1 http://…/isbn978 description hasReviewer Awesome Book http://…/reviewer name Juan Sequeda sameAs http://juansequeda.com/id http://dbpedia.org/Austin livesIn name Juan Sequeda

  18. Linked data = internet + http + RDF hasReview http://…/review1 http://…/isbn978 Programming the Semantic Web description title hasReviewer sameAs Awesome Book author http://…/isbn978 Toby Segaran http://…/reviewer name isbn 978-0-596-15381-6 Juan Sequeda publisher sameAs http://…/publisher1 name O’Reilly http://juansequeda.com/id http://dbpedia.org/Austin livesIn name Juan Sequeda

  19. Linked data = internet + http + RDF

  20. Linked Data Principles • Use URIs as names for things • Use URIs so that people can look up (dereference) those names. • When someone looks up a URI, provide useful information. • Include links to other URIs so that they can discover more things.

  21. Web as a database Linked Data makes the web exploitable as ONE GIANT HUGE GLOBAL DATABASE!Is there any query language like sql?SPARQL…

  22. May 2007

  23. What is a Linked Data application/service? Software system that makes use of data on the Web from multiple datasets and that benefits from links between the datasets

  24. Characteristics of Linked Data Applications • Consume data that is published on the web following the Linked Data principles: an application should be able to request, retrieve and process the accessed data • Discover further information by following the links between different data sources: the fourth principle enables this. • Combine the consumed linked data with data from sources (not necessarily Linked Data) • Expose the combined data back to the web following the Linked Data principles • Offer value to end-users

  25. the 5 stars of open linked data ★make your stuff available on the Web (whatever format) ★★make it available as structured data (e.g. excel instead of image scan of a table) ★★★non-proprietary format (e.g. csv instead of excel) ★★★★use URLs to identify things, so that people can point at your stuff ★★★★★link your data to other people’s data to provide context http://lab.linkeddata.deri.ie/2010/star-scheme-by-example/

  26. Two magics of Web Science: the case of Linked Data

  27. The (practical) question contextualized & hands-on experience in Semantic Web & Business 3.0 on a unique, fast evolving and semantified dataset

  28. PSGR project: the answer The first attempt to generate, curate, interlink and distribute daily updated public spending data in LOD formats that can be useful to both expert (i.e. scientists and professionals) and naïve users.

  29. The context first…

  30. Economy after the Web New form of property • Public, Private, Peer (e.g. Wikipedia) The right to: • Use-modify-benefit-transfer resources • Energetic & connected consumption • Pro-sumption

  31. Research question Web economy: from potential to actual Enable new virtuous cycles in the economy through Linked Open Data

  32. Outline • EU Unification: institutions-technology • Why Linked Open Data? • Economic LOD • the story so far • how to start • use cases • engineering • Government Budget • Tenders • Spending • Business Information • Next steps

  33. EU Unification: the institutions Best in theory – poor in practice a (complicated) market example • monetary policy, currency, eurozone • European Single Market • fiscal policy FORTHCOMING

  34. EU Unification: the technology Linked Data or Web of data • “publish once, use many times”. • different consumers extract different slices of the data for different purposes • publish in context: value & “meaning”

  35. EU Unification: the technology • Linked Data (LD) + Open Data =LOD • Economic LOD as “data currency”

  36. Why LOD? • Transparency & innovation Network effects: enabling users to • bidirectional & massively processable interconnections among data • re-using the existing infrastructure in the government and business spheres

  37. Economic LOD: the story so far • Isolated/fragmented behind technological & institutional barriers • General statistics: Eurostat etc. • LOD2 case • LOTTED (Linked Open Tenders Electronic Daily)

  38. Economic LOD: how to start A general model

  39. Economic LOD: use cases • Business applications on top • Users: citizens, gov., EU, business • track the life-cycle of every financial flow: evaluate budget allocation, tenders, spending and their efficiency • pre-allocate resources on provisional public works • receive & submit information in real-time

  40. Economic LOD:engineering

  41. Government Budget • heterogeneous repositories & methods (mainly PDF)

  42. Tenders • Closed data in HTML • Public Contracts Ontology (PCO), e.g. • pco:Contract and pco:AwardCriterion • Common Procurement Vocubulary • now working on linking our ontology to: • Payments Ontology • GoodRelations • FOAF

  43. Spending • most dynamic & open part • increasing number of countries/cities • raw & structured data • leader: the Greek Clarity project • spending decisions ex-ante to execution • Actually every decision

  44. www.publicspending.gr (*****) • based on Greek Clarity & Tax information • semantify, interconnect, clean, visualize, SPARQL endpoint, daily update • PSGR ontology Links to • WESO products classif. • UK Payments Ontology • DBpediaand Geonames • …more to come

  45. Business Information • Registries: mainly closed • Key standards • Classification of Products by Activity (CPA) • eXtensible Business Reporting Language (XBRL)

  46. Business Information

  47. Next steps • Working on our basic ontology • Real-life examples & apps • Bad news: A long way to go • Good news: we have started

  48. PSGR • why Linked Open Data (LOD) • LOD in Greece • issues • WHERE MY MONEY GOES App • local spending in EU demo • to the future

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