Dbrev dreaming of a database revolution
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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

DBrev: Dreaming of a Database Revolution

Gjergji Kasneci, Jurgen Van Gael, Thore Graepel

Microsoft Research

Cambridge, UK


Uncertainty in applications

Uncertainty in Applications

Intelligent data management with following requirements:

  • Store, represent, retrieve data

  • Assess accuracyand confidence

  • Self diagnostic and calibration

+

DB & IR

Statistical ML


Main issues

Main Issues

Outrageous:

solve these problems simultaneously in integrated system…

 DBrev


Dbrev exploits large scale graphical model

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


Dbrev on information extraction and integration

DBrev on Information Extraction and Integration

Provenance through factor graphs in DBrev:


Dbrev on information extraction and integration1

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


Dbrev on information extraction and integration2

DBrev on Information Extraction and Integration

Ambiguity & Context in DBrev:


Dbrev on information extraction and integration3

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


Dbrev on information extraction and integration4

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)


Dbrev on information extraction and integration5

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)


Dbrev on information extraction and integration6

DBrev on Information Extraction and Integration

Retrieval & Discovery in DBrev:

partnerOf

locatedIn

Microsoft

$x

US

certifiedBy

SPARQL / Conjunctive Datalog / NAGA


Dbrev on information extraction and integration7

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


Summary

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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