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On the Quest for Changing Knowledge
Marco Brambilla, Stefano Ceri, Florian Daniel, Emanuele Della Valle
@marcobrambi
Data-driven innovation
and
Innovation-driven data
Innovation requires
Precise
To the point
Up-to-date
Domain-specific
information
There are more things
In heaven and earth, Horatio,
Than are dreamt of in your philosophy.
Shakespeare (Hamlet Act 1, scene 5)
From Data to Wisdom
Formalizing new knowledge is hard
Only high frequency emerges
The long tail challenge
Knowledge Extraction
Text mining
Semantic Web
Search and recommendation systems
No specific care for emerging knowledge
Heaven and Heart
How to peer through an effective window
on real world?
Social media, our blessing and curse
Domain experts matter
Can we use social networks to
discover emerging knowledge?
Beware the streetlamp effect
The bias of the source
The bias of the observer
Famous Emerging
Evolving Knowledge
consolidated
knowledge
social
content
factoid
a
c
¬c
bpotentially
emerging potentially
decaying
Overview
Knowledge Enrichment Setting
HF Entity1 HF Entity5
HF Entity2 HF Entity4
HF Entity3
LF Entity1
??
LF Entity2 LF Entity4
LF Entity3
??
High Frequency
Entities
Low Frequency
Entities
??
?? ????
??
Type1
Type11
Type2
Type111
Instances Types
<<instanceof>>
<<instanceof>>
<<instanceof>>
<<instanceof>>
<<instanceof>>
<<instanceof>>
??
??
??
??
??
Seed Entity
Seed
Type
Type of
interest
Legend
Expert inputs
Enrichment problems
Property2
Relations HF - LF entities
Relations LF - LF entities
Typing of LF entities
Extraction of new LF entities
Property1
?? ?? ??
Finding attribute values
Emerging Knowledge Harvesting
Domain Types
Types selected by the experts
Relevant for the domain
Seed characterization
Selected by the expert
Belonging to an expert type
Thoroughly Described
# @ a w
Social Media Sourcing
Content coming from the seeds’ accounts
Candidate Selection
Potentially any entity extracted from
the social streams
Resulting in huge sets of candidates
# @ a w ♥
Candidate Typing
Candidate Pruning
Initial pruning of candidates based on
TF-DF:= df * tf / (N – df +1)
(*) variant of TF-IDF that does not discount document frequency because we
are actually happy about frequent appearance
(we don’t look for information entropy!)
Candidate Ranking
Candidate Vector Space
Purely syntactic
Semantic:
Based on entity extraction / DBpedia
Based on deep learning on images / ClarifAI
Example Analysis
Experiments
Fashion brands
Writers
Painters
Exhibitions
4,400 strategies evaluated
44 alternative feature vectors
(12 basic features and 32 aggregations)
9 different weighting values for aggregations
5 levels of recall for entity extraction
3 different distances
Pruning Phase
From 4,400 down to 10 strategies
Eliminating the less relevant parameters
Italian Fashion Brands
Precision @5 = 0.2
Increasing # seeds reduces precision
Australian Writers – 22 seeds
Precision @5 = 0.8
Innovative Painters – 21 seeds
Precision @5 = 0.6
Twitter vs. Instagram
P@5 = 1.0
P@5 = 0.8
vs.
Fashion: Twitter + Instagram
&
&
Writers: Twitter + Instagram
Prec. = 1
Conclusion
It’s about time
to build innovation based on data
and build knowledge based on innovation
Harvesting can be iterative
On the Quest for Changing Knowledge
contact us
Marco Brambilla, @marcobrambi, marco.brambilla@polimi.it
http://datascience.deib.polimi.it

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