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Data aware apps

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What it takes to build data aware applications. …

What it takes to build data aware applications.
Why Web Interoperability is broken?

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  • We’ve overcome the first two complexity barriers by introducing standard protocols, encoding and formats for representing information.The next step would be establishing an abstraction layer and standards for meaning.Once this is done, web APIs and data sources can exchange information on the meaning level, and do this transparently.Data driven applications are expressed as data flow graphs, and the problem of building these applications is reduced to a path finding problem.
  • A typical data flow, starts from a set of inputs, goes through a transformation & processing flow, and then output as a machine readable, human readable format, persisted or used as a trigger for external applications.Sensors can be for example the user’s GPS coordinates.
  • Personal email: mesagie [at] gmail [dot] com
  • Transcript

    • 1. Data-aware appsChallenges & Opportunities
      Sagie Davidovich
      VP R&D
    • 2. 2011 - Web Interoperability is broken
      • APIs:
      • 3. Too many of them
      • 4. Not aware of each other
      • 5. APIs Operate on the keyword level
      • 6. No standard ontology
      • 7. Different formats
      • 8. TOS/rate limits /credibility are notmachine readable
      • 9. Discovery doesn’t exist (WSDL failed)
      • 10. Too few know RDF/OWL/SPARQL
      • 11. 99.9% of the information on the web is free form text
      • 12. Web data is partial, noisy, inaccurate, not objective
    • Abstraction stack - towards data aware apps
    • 13. A typical data flow
      Input
      Process
      Output
      XML/
      JSON
      Order
      Search
      Filter
      Transform
      Data aware UI components
      Interaction
      Aggregate
      Format
      Persist
      Sensors
      Generate
      Compute
      Augment
      Stream
      Trigger
    • 14. Data aware web - Pragmatic approach
      Data aware app host
      Today’s web
      Semantic Abstraction layer
      Reasoner & data flow manager
      Query Enrichment
      APIs
      Result refinement
      Abstractions Library
      Synthesis
      Unstructured Web pages
      Semantic Store
      Entity & Fact extraction
    • 15. Abstractions Libraryovercoming the complexity barrier
      Lunchsuggest = Meals(familynearby), Meals(friendsnearby)
      Meals(folks) = folks/foodtaste/common/nearByRestaurants/deals
      FamilyNearby= facebook:connections/filter(family, near)
      NearMe(object) = |object/location - me:location| < 10km
      location= sensor:gps
      family = children, spouse, parents, …
    • 16. What we’ve done at SemantiNet
      High performance graph storefor Wikipedia & Linked-data
      Extensible & Dynamic query enginewith a simple XPath-like query language
      Semantic abstraction layer over APIsincludes semantic query refinements
      A complete stack of NLP librariesinteroperable with other APIs
      Ontology aware contextual disambiguation
      Templating engine
      Web development environment for data aware apps
    • 17. Thank you
      More coverage on:
      semanticweb.com/build-data-aware-apps-without-the-hassle_b17465
      blog.headup.com
      Sagied [at] semantinet [dot ] com