Constructing Folksonomies from UserSpecified Relations on Flickr
Anon Plangprasopchok and
Kristina Lerman
(WWW 2009)
1
Motivation
Annotation /
Metadata
Produce
Users
Web content
hierarchical classification
Organize
Search
Recommend
Leverage
Categorize
2
Motivation
• Metadata from an individual user may
be too inaccurate and incomplete…
•The metadata from different users may
complement each other, making it,
in combination, meaningful.
Goal: to induce category knowledge from
social annotation produced by many users
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Folksonomy
• Original definition: classification emerging
from the use of tags by users (Thomas
Vander Wal)
• In this work: hidden classification
hierarchies from annotation created by
many users
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Hierarchical Relations in Social Web
• Appear Implicitly
Tags:
Insect
Goal: to induce deeper
Grasshopper
hierarchies from this
Australian
metadata
Macro
Orthopteran (直翅類)
• Appear Explicitly
Folder (collection)
Sub folder (set)
Relations
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Outline
•
•
•
•
•
Motivation
Approaches
Results
Discussion
Related work
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Inducing Hierarchy from Tags
Existing approaches
• Graph based (Mika05)
• build a network of associated tags (node = tag, edge = cooccurrence of tags)
• suggest applying betweenness centrality and set theory to
determine broader/narrower relations
• Hierarchical Clustering (Brooks06; Heymann06+)
•Tags appear more frequently would have higher centrality and
thus more abstract.
• Probabilistic subsumption (Sanderson99+, Schmitz06)
• x is broader than y if x subsumes y
x
• x subsumes y if p(x|y) > t & p(y|x) < t
y
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Inducing Hierarchy from Tags
• Some difficulties when using tags
to induce hierarchy:
Notation: A  B
(A is broader than B)
(hypernym relation)
Washington  United States
Car  Automobile
Specificity  Rarity
Insect  Hongkong
Color  Brazilian
Tags are from different facets*
Above relations induced using subsumption approach on tags [Sanderson99+, Schmitz06]
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Inducing Hierarchy from user-specified relations
• User specified relations, e.g.,
– Flickr’s Collection-Set ,
– Delicious’ Bundle-Tag,
– Bibsonomy’s Relation-Tag
• Key intuition: Not so many people specify
peculiar relations like
– “automobile”  “car”, or
– “Washington”  “United States”
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Simple Strategy
Collection
Sets
Concept relations
netherland
Collection
The Netherlands - Holanda
holanda
blijdorp
holanda
blijdorp
rotterdam
netherland
Set
Tokenize + Stem
Blijdorp - Rotterdam
countri
rotterdam
netherland
1. Remove “noisy” relations
- Conflict resolution
- Significance test
countri
holland
netherland
blijdorp
china
……
2. Link concepts & Select path
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Remove noisy relations:
1st approach
• Conflict Resolution (when both a->b and b->a appear)
– Relation conflicts occur because of noise
– Voting scheme:
Keep ab (and discard ba)
If Nu(ab) > 1 and Nu(ab) > Nu(ba)
insect
butterfly
2
10
butterfly
insect
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Remove noisy relations:
2nd approach
• Significance Test
- Use statistical significance test to decide if a  b is
significant
- Null hypothesis: observed relation ab was
generated by chance, via the random, independent
generation of individual concepts a, b (according to
the binomial distribution).
Is “b” narrower than “a” by chance?
accept
reject

# observations
# of ab
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Link concepts and select path
• Link concepts: assume that same terms refer to the same concept.
anim
anim

+
bug
anim
insect
bug
insect
• Select path: link relations from many users can cause a spaghetti graph
4 possible paths from anim  moth:
1) abim
2) aim
3) am
4) abm
anim
26
72
1
insect
bug
Network Bottleneck idea:
“the flow bottleneck is a minimum flow capacity
among all relations in the path”
10
18
4
moth
1)
2)
3)
4)
abim [BN score = min(26,1,18) = 1]
aim [BN score = min(72,18) = 18]
am [BN score = min(10) = 10]
abm [BN score = min(26,4) = 4]
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Contribution#2:Learning Concept Hierarchies
Evaluation & Data Set
•
Hypothesis: the approach that takes explicit relations into account can
induce better hierarchies.
•
“Better” means more consistent with hand-built hierarchies (ODP ver.
10/08)
•
The baseline approach is subsumption approach [Schmitz06]
Collection and set terms are used instead of tags, making it comparable.
Data Set:
 Data from 17 user groups, devoted to wildlife
and naturalist photography
 21,792 of 39,922 users specify at least one collection
 110,543 unique terms (c.f. 166,153 unique terms in ODP),
15,495 terms in common.
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Contribution#2:Learning Concept Hierarchies
Evaluation methodology
ODP has many sub hierarchies: comparing to the induced ones are impractical!
It’s easier to compare when specifying “root concept” and “leaf concepts”, i.e.,
specifying a certain sub tree to compare.
Relations (right after tokenized)
Reference hierarchy
(ODP)
Induced hierarchy
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Contribution#2:Learning Concept Hierarchies
Metrics
• Taxonomic Overlap [adapted from Maedche02+]
– measuring structure similarity between two trees
– for each node, determining how many ancestor and
descendant nodes overlap to those in the reference
tree.
• Lexical Recall
– measuring how well an approach can discover
concepts, existing in the reference hierarchy
(coverage)
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Quantitative Results
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Contribution#2:Learning Concept Hierarchies
Quantitative Results
• Manually selecting 32 root nodes
• Taxonomic Overlap :
• 27 of them are better than those by subsumption
• 3 of them get zero score in both approaches
• Lexical Recall:
• 28 of them are better than those by subsumption
• 2 of them get similar score on both approaches
• the rest, by subsumption, only induce the root node.
• The proposed approach can induce deeper trees
The proposed approach can induce hierarchies more
consistent with ODP in almost all cases.
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Sport hierarchy
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Invertebrate hierarchy
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Country hierarchy
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Discussion
• Simple strategy to aggregate a large number of shallow
relations specified by different users into a common,
deeper hierarchy
• Induced hierarchies are more consistent with ODP
• Future work includes:
Term ambiguity
Relation types
Global path
Apply to other datasets
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Thank You!
&
Questions?
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