1. An Integrated Approach
to Discover Tag Semantics
SAC 2011, Web Technologies Track, March 24th 2011
Antonina Dattolo Davide Eynard Luca Mazzola
University of Udine USI - University of Lugano USI - University of Lugano
Department of Mathematics ITC - Institute for ITC - Institute for
and Computer Science Communication Technologies Communication Technologies
antonina.dattolo@uniud.it davide.eynard@usi.ch luca.mazzola@usi.ch

2. Talk outline
 Properties of tags
 Folksonomies as edge-colored multigraphs
 Framework design and implementation
 Tests and evaluations
 Conclusions
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3. Tags properties
Tags:
 are democratic and bottom-up (vs hierarchical)
 are inclusive and current
 follow desire lines
 are easy to use
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4. Tags cons
 Lexical ambiguities:
 Synonyms
 game and juego, or web2.0 and web_2
 Homonyms
 check as in chess and in “to check” (polysemous)
 sf as scifi or san_francisco
 Basic level variations
 dog and poodle
 Ambiguities due to different purposes:
 blog to tag a blog software (i.e. Wordpress), a blog service, a blog
post, something to blog later, ...
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5. Advantages of disambiguation
 Synonym detection:
 increases recall
 allows for better recommendation systems
 Homonym detection:
 allows to find different contexts of use
 increases precision
 Basic level variations detection:
 identifies a hierarchy
 increases recall (i.e. automatically searching for subclasses)
 provides a mean to browse search results
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6. Approaches to tag disambiguation
 Roughly two main families of approaches
 Theoretical ones, aiming at describing the system as a
whole
 More practical, ad-hoc ones (often addressing one or few
issues at a time)
 Our approach
 Main assumption: lexical ambiguities are not independent
from each other
 Solution based on
 a theoretical framework
 a modular, extensible analysis tool
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7. Folksonomies as edge-colored
multigraphs
 Def.1: An edge-colored multigraph is a triple
ECMG = (MG, C, c)
where:
 MG = (V,E,f) is a multigraph
 C is a set of colors
 c : E→C is an assignment of colors to multigraph edges
 Def.2: A personomy related to user u is a non-directed
edge-colored graph of color Cu:
Pu = (T, R, E, Cu)
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8. Folksonomies as edge-colored
multigraphs
 Def.3: Given a set of users U and the family of
personomies Pu (u ∈U), a folksonomy is defined as
that is, an edge-colored multigraph where:
 vertices are tags + resources
 edges are tag assignments made on
resources by each user
 every color is a different user
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9. First simplification step
 As we are only interested in relationships between
tags, we need to perform two simplification steps on
the edge-colored multigraph
 Step 1: colored edges are collapsed and substituted
by weighted edges
 potentially, every color (user) might be
assigned a different weight wu
 the weight w of the collapsed edge is the sum
of all the wu linking the same two vertices
 when wu= 1 for each user, w = times a tag is
used on a resource
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10. Second simplification step
 Step 2:
 a link is created between ta and tb if they
share a resource
 resource nodes are dropped
 Edges' weights can be calculated
in different ways:
 number of triples (ti ,r,tj ) where (ti ,r), (r,tj ) ∈E
=> co-occurrence
 normalized co-occurrence (i.e. Using the
Jaccard index)
 distributional measures
 custom metrics (i.e. sum of products of
connecting edges' weights) =>
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11. The whole process at a glance
1 2
3 4
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12. System architecture
 Basic assumption:
 ambiguous tags should be related (either by cooccurrence or
by presence in the same context)
 Three main components:
 tag analysis tool
 disambiguation tool
 front-end
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13. Synonyms detection / 1
 Natural text …
 Two words are considered synonyms if they can be replaced
by each other without affecting the meaning of a sentence
 … vs. Tag-based systems
 It is possible to swap two tags within a “sentence” (i.e. a
tagging action) without affecting its meaning when we have:
 variations of a word (i.e. blog, blogs, blogging)
 translations into other languages (i.e. game, juego, spiel)
 terms joined by non-alphabetic characters (i.e. web2, web_2)
 No “one size fits all” solution
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14. Synonyms detection / 2
 A modular solution for synonyms detection:
 different heuristics, each one returning the likelihood of tags to be
synonyms
 results are weighted to obtain an overall likelihood
 Suggested heuristics:
 an edit distance such as Levenshtein's (normalized to account for short
strings);
 synonym search in WordNet (good precision, low recall);
 online translation bases (top-down, such as dictionaries, or bottom-up,
collaboratively grown vocabs like Wikipedia)
 stemming with NLP algorithms
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15. Homonyms detection
 Check if the tag t has been used in different contexts
 cluster tags related to t in groups
 the most frequent tags in these groups are used to name
and disambiguate the contexts
 Clustering algorithm:
 an overlapping one, also used in social network analysis*
 a cluster is a subgraph G identified by the maximization of a
fitness property
s = strength of internal (in)
or external (out) links
α = tweaking parameter
* A. Lancichinetti et al. : “Detecting the overlapping and hierarchical community structure of complex networks”
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16. Hierarchy detection
 Hierarchy is a specific case of basic level variation
 A possible approach: Hearst patterns on the Web,
such as:
 C1 (and|or) other C2 (i.e. “poodles and other dogs”)
 C1 such as I (i.e. “cities such as San Francisco”)
(note: Ci are concepts, I is a concept instance)
 Search for the patterns, and use the number of results
as an indicator for their strength
 Pros: the Web is as up-to-date as folksonomies

Cons: O(n2) complexity, not really scalable
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17. Prototype development
 Dataset
 Data from more than 30K users of
http://www.delicious.com
 Ignored the system:unfiled tag
 For the calculation of Tag Context Similarity,
we only took into account the top 10K tags
 Prototype
 Tag analysis tool, calculating CO, NCO, and TCS (takes time, runs as a
batch job and saves matrices in the DB)
 Disambiguation with homonyms plugin, implementing the overlapping
clustering algorithm, and Wikipedia synonym discovery
 Front-end is currently a command-line application
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18. Experimental results / 1
 System tested against three different sets of tags:
 Top 20 tags in delicious
 A group of tags known to be ambiguous (apple, cambridge, sf,
stream, turkey, tube)
 A set of subjective tags, chosen between the most popular ones in
delicious (cool, fun, funny, interesting, toread)
 For each tag:
 we calculated the top n (with n = 50) related tags with the three metrics
(CO, NCO, TCS)
 we performed synonym and homonym analyses
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19. Experimental results / 2
 Tag Context Similarity already tends to provide
synonyms as top-related tags
 i.e. toread related: read, read_later, to_read, etc.
 Analyzing a less popular synonym (@readit):
 9 out of the top 10 (and 17 out of the top 50) related tags are synonyms
 reason: as less popular tags are less spread across contexts, they tend
to have a higher similarity with other less popular synonyms
 Wikipedia results:
 analyzing the 31 tags in our three sets, we got 215 new words;
 of those 215, only 83 are valid tags in our delicious dataset;
 of those 83, only 20 belong to the 10K most-used tags;
 only 2 belong to the set of the top-related tags of their English synonym.
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20. Experimental results / 3
 Homonyms detection:
 we tested the algorithm with
different values of α
 meaningful results in a relatively
short time (but we are working
only on the top related tags...)
 limit: the graphs of top related
tags differ in connectivity, so
there is not a value of α that is
good for all of them (αsf=1.4,
αstream=1.74).
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21. Conclusions
 Model
 Flexible enough to support other kind of metrics
 Multigraph can be simplified in other ways
 User-related weights still have to be taken into account
 Tool
 Still in prototypal phase, but already provided useful results
and allowed us to compare
 metrics: different metrics provide very different results, that might be
more or less useful according to the user needs
 tag behaviors: different depending on their popularity and the use
that people do of them
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22. Conclusions
 Ongoing work
 Clustering evaluation metrics to find best α
 Applications (i.e. for tag grouping and visualization*)
 User- and resource-specific projections**
 Future work
 Development of other plugins and front-end
 Play with user-related weights to focus on specific
communities / filter spam
* Mazzola, Eynard, Mazza: ”GVIS: a framework for graphical mashups of heterogeneous sources to support data
interpretation”.
** Dattolo, Ferrara, Tasso: "On social semantic relations for recommending tags and resources using folksonomies"
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23. Thank you!
Thanks for your attention!
Questions?
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24. toread top 20 related tags
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25. @readit top 20 related tags
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26. sf top 20 related tags
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27. stream top 20 related tags
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