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