Gjergji Kasneci Jurgen Van Gael Thore Graepel Microsoft Research Cambridge UK Uncertainty in Applications Intelligent data management with following requirements Store represent retrieve data ID: 593003
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Slide1
DBrev: Dreaming of a Database Revolution
Gjergji Kasneci, Jurgen Van Gael, Thore Graepel
Microsoft Research
Cambridge, UKSlide2
Uncertainty in Applications
Intelligent data management with following requirements:
Store, represent, retrieve data
Assess accuracy
and confidence
Self diagnostic and calibration
DB & IR
Statistical ML
+Slide3
Main Issues
Outrageous: solve these problems simultaneously in integrated system…
DBrevSlide4
DBrev Exploits Large-Scale Graphical Model
Combine logical constraints and sources of evidence about knowledge fragments into belief
n
etwork, e.g.:
Sample Belief Network for Aggregating User Feedback and Expertise on Knowledge Fragments,
Kasneci et al.: WSDM’11Slide5
DBrev on Information Extraction and Integration
Provenance through factor
graphs in DBrev: Slide6
DBrev on Information Extraction and Integration
f
1
<
MichaelJackson
,
diedOn
,
25-07-2009>
<
MichaelJackson
,
livesIn
,
Ireland>
wikipedia.org/wiki/Michael_Jackson
michaeljackson.com
f
2
f
1
’
m
ichaeljackson
-
sightings.com
Provenance through
factor
graphs in DBrev: Slide7
DBrev on Information Extraction and Integration
Ambiguity & Context in DBrev: Slide8
DBrev on Information Extraction and Integration
Ambiguity & Context in DBrev:
f
Statistical fingerprint
derived from the Web
Ontological description/
Semantic features
Entity
f’
Entity1
Entity2
s
ameAsSlide9
DBrev on Information Extraction and Integration
Consistency in DBrev:
<A,
R
, B>
^
<B,
R
, C> ^ <R, type, Transitive>
<A, R, C>
refersTo
(“x”, A) ^ refersTo
(“y”, C) ^ canBeDeduced(A
, R,
C)
refersTo (“r”, R)Extracted Triple: (“x”, “r”, “y”)Slide10
DBrev on Information Extraction and Integration
Consistency in DBrev:
<A,
R
, B>
^
<B,
R
, C> ^ <R, type, Transitive>
<A, R, C>
refersTo
(“x”, A) ^ refersTo
(“y”, C) ^ canBeDeduced(A
, R,
C)
refersTo (“r”, R)Extracted Triple: (“x”, “r”, “y”)
^
^
vSlide11
DBrev on Information Extraction and Integration
Retrieval & Discovery in DBrev:
Microsoft
$x
US
locatedIn
certifiedBy
partnerOf
SPARQL / Conjunctive
Datalog
/ NAGASlide12
DBrev on Information Extraction and Integration
Retrieval & Discovery in DBrev:
Approximate Matching
Entity / relationship similarity
Reasoning over relationship properties
Reasoning with temporal / spatial
constraints
User Preference
Information needs
freshness, accuracy, popularity
Interests
context, background, current interest
Microsoft
$x
US
locatedIn
certifiedBy
partnerOf
SPARQL / Conjunctive
Datalog
/ NAGASlide13
SummaryDBrev builds on large-scale factor graph to simultaneously approach:
provenance
context
ambiguity
consistency
Retrieval &
Discovery
An inspiration to combine…
… for the challenges ahead.
DB & IR
Statistical ML
+