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DBrev: Dreaming of a Database Revolution

DBrev: Dreaming of a Database Revolution. Gjergji Kasneci, Jurgen Van Gael, Thore Graepel Microsoft Research Cambridge, UK. Uncertainty in Applications. Intelligent data management with following requirements:. Store, represent, retrieve data. Assess accuracy and confidence.

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DBrev: Dreaming of a Database Revolution

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  1. DBrev: Dreaming of a Database Revolution Gjergji Kasneci, Jurgen Van Gael, Thore Graepel Microsoft Research Cambridge, UK

  2. Uncertainty in Applications Intelligent data management with following requirements: • Store, represent, retrieve data • Assess accuracyand confidence • Self diagnostic and calibration + DB & IR Statistical ML

  3. Main Issues Outrageous: solve these problems simultaneously in integrated system…  DBrev

  4. DBrev Exploits Large-Scale Graphical Model Combine logical constraints and sources of evidence about knowledge fragments into belief network, e.g.: Sample Belief Network for Aggregating User Feedback and Expertise on Knowledge Fragments, Kasneci et al.: WSDM’11

  5. DBrev on Information Extraction and Integration Provenance through factor graphs in DBrev:

  6. DBrev on Information Extraction and Integration Provenance through factor graphs in DBrev: <MichaelJackson, diedOn, 25-07-2009> <MichaelJackson, livesIn, Ireland> michaeljackson.com f1’ f1 f2 michaeljackson- sightings.com wikipedia.org/wiki/Michael_Jackson

  7. DBrev on Information Extraction and Integration Ambiguity & Context in DBrev:

  8. DBrev on Information Extraction and Integration Ambiguity & Context in DBrev: Entity1 f sameAs f’ Ontological description/ Semantic features Statistical fingerprint derived from the Web Entity Entity2

  9. DBrev on Information Extraction and Integration Consistency in DBrev: <A, R, B> ^ <B, R, C> ^ <R, type, Transitive>  <A, R, C> Extracted Triple: (“x”, “r”, “y”) refersTo(“x”, A) ^ refersTo(“y”, C) ^ canBeDeduced(A, R, C)  refersTo (“r”, R)

  10. DBrev on Information Extraction and Integration Consistency in DBrev: ^ ^ <A, R, B> ^ <B, R, C> ^ <R, type, Transitive>  <A, R, C> Extracted Triple: (“x”, “r”, “y”) v refersTo(“x”, A) ^ refersTo(“y”, C) ^ canBeDeduced(A, R, C)  refersTo (“r”, R)

  11. DBrev on Information Extraction and Integration Retrieval & Discovery in DBrev: partnerOf locatedIn Microsoft $x US certifiedBy SPARQL / Conjunctive Datalog / NAGA

  12. DBrev on Information Extraction and Integration • Approximate Matching • Entity / relationship similarity • Reasoning over relationship properties • Reasoning with temporal / spatial • constraints Retrieval & Discovery in DBrev: partnerOf locatedIn • User Preference • Information needs • freshness, accuracy, popularity • Interests • context, background, current interest Microsoft $x US certifiedBy SPARQL / Conjunctive Datalog / NAGA

  13. Summary DBrev builds on large-scale factor graph to simultaneously approach: Retrieval & Discovery provenance context ambiguity consistency An inspiration to combine… + DB & IR Statistical ML … for the challenges ahead.

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