1. Visualizing and Managing Folksonomies
Antonina Dattolo { Emanuela Pitassi
University of Udine, Via delle Scienze 206, I-33100 Udine, Italy
June 25, 2011

2. Outline
Background
Open Issues
Our Proposal
Folkview: the formal model
Folkview views and authoring
Related works
Conclusion and future work

3. Background
I The advent of Web 2.0 shifts the task of classifying resources
from a reduce set of experts, to the wide set of Web users.
I Free classi

4. cations are made by user thanks to tags, which
generate a folksonomy (Vander Wal, 2004; Mathes, 2004)
I Several social tagging systems such as Bibsonomy (Hotho et
al, 2006) or delicious allow users to classify resources and
generate folksonomies, but they are dicult to manage,
modify and visualize in dynamic and personalized ways.

5. Background: some examples
I The creation of personalized views, which may display a
limited, well de

6. ned and personalized sub-portion of an
entire hyperspace has already been considered in dierent
settings.
I Adptive bookmarking systems, e.g.:
I PowerBookmarks (Li et al, 1999)
I Siteseer (Rucker et al, 1997)
I Web Tagger (Keller et al, 1997)
I Start pages on Web browser, e.g.:
I Netvibes (http://www.netvibes.com.it)
I My Yahoo (http://my.yahoo.com)
I iGoogle (http://www.google.it/ig)

7. Open Issues
I Social Tagging Systems suer from dierent issues:
I the lack and the exigence of general methotodologies for
extracting semantic information (Dattolo et al., 2010)
I the lack and the exigence of personalized and dynamic
workspaces in wich users
I Can visualize personalized views of the folksonomy
I Can apply personal changes
I A crucial task for developers of current Web application is
how to model and create speci

8. c tools for providing
personalized views to the users.

9. Open Issues
I A folksonomy is usually represented by a tripartite graph or
network Various researches have dealt with the issue related
to the complexity of the nature of the graph itself, projecting
a folksonomy on simpli

10. ed structures (Lambiotte, 2006;
Dattolo, 2011).
I Generally a folksonomy is represented by a tag-cloud, but this
kind of visualization is not sucient as the sole means of
navigation (Sinclair, 2007).
I Possible multiple visualizations of a folksonomy allow users
to:
I have a more eective comprehension of the semantic relations
of a folksonomy
I have a more useful navigation through the involved elements
I manipulate the existing relations among tags and resources
according to the user needs.

11. Our Proposal
I The main aim of this work is to propose and describe a novel,
distributed, modular system called Folkview, whereby a
folksonomy is conceived dynamically through the use of
multiple agents.
I These agents will be capable of
I managing the structural and semantic properties;
I cooperating for obtaining common objectives;
I oering personalized and dynamic views.
I Steps:
I De

12. nition of the Formal Model which exploits multi-agents
system
I Personalized views Folkview Prototype

13. The formal model
I Traditionally, given the sets of users U , tags T and and
resources R , a folksonomy is de

14. ned as the set of tag
assignments
(u ; r ; t ) P U ¢ T ¢ R
i j k
where i = 1; : : : ; jU j; j = 1; : : : ; jT j; k = 1; : : : ; jR j, each of
them indicating that user u has tagged the resource r with
i j
t .
k
I User pro

15. les, functions, metrics or semantic relations among
users, tags, resources and tas are not intrinsic properties of
the folksonomy.

16. The structural components of the folksonomy
I In order to de

17. ne a F , we identify three classes of sets:
I T 2 T is the set of tags used by u on r ;
ui ;rj i j
I R 2 R is the set of resources tagged by u with t ;
ui ;tk i k
I U is the set of users that tagged r with t .
tk ;rj j k
I Each set represents a structural component of the folksonomy,
and we call it structural ; the tags are grouped associating to
them a semantic label for identifying their meaning in that
dimension.

18. Our representation of a folksonomy
Figure: 6 structural dimensions (left) and the corresponding folksonomy
(right)
The

19. gure above represents three linear paths that contain the
resources tagged by user u , using respectively t , t and t .
1 1 2 3
The labels associated with them are respectively u ; t , u ; t and
1 1 1 2
u ; t , and represent a sub-portion of her personomy.
1 3

20. De

21. nition of Structural Dimension and Static Folksonomy
De

22. nition
A structural dimension is a labeled path
Dui ;rj = (V ; E ; )
where
I V =T is the set of vertices,
ui ;rj
I E is the set of edges,
I (e ) = (u ; r ) Ve P E is an edge labeling, and
degree (t ) = 0; 1; 2 Vt P T
i j
k . k ui ;rj
In particular, degree (t ) = 0; 1; 2 only if jT j = 1.
k ui ;rj
De

23. nition
A static folksonomy F is a labeled multigraph given by the union
of three families of structural dimensions.
F =
[ Dui ;rj ‘
[ Dui ;tk ‘
[ Dtk ;rj
i ;k i ;j j ;k

24. A dimension as an agent
We can introduce the de

25. nition of the dimension h ui ; r j based on
the structural dimension D . ui ;rj
De

26. nition
A dimension h = (Ts ; En; Re ; Ac ) is an agent where
I Ts = D ; ui ;rj
I En = fu ; r ; t ; : : : ; t g;
i j 1 n
I Re = fYg, initially;
I Ac = fadd -tag ; delete -tag ; modify -tag ; : : : g
Analogously, we can de

27. ne new classes of agent dimensions, not
only for structural dimensions. New dimensions can be created
directly from the user, or computed by the system applying speci

28. c
metrics, or generated applying ontological models:
I each dimension can contain other dimensions; dimension
associates a semantics to the set of grouped entities.

29. Folksonomy de

30. nition as a multi-agent system
De

31. nition
A folksonomy F is a multi-agent system formally described as a
labeled multigraph of agent entities, organized in semantic
contexts, called dimensions.
p=
[h
n
i
i =1
All in a folksonomy is a computational agent, equipped with a set
of local variables, that de

32. ne its internal state, and a modular and
extensible set of procedural skills.

33. Folkview views and authoring
In the labeled multigraph contained in the de

34. nition of p we can
recognize the zz-structures (Nelson, 2004): they are
non-hierarchical, minimalist, scalable structure for storing,
linking and manipulating dierent kind of data.
From these structures, we inherit many strengths, such as their
intrinsic capability to preserve contextual interconnections
among dierent information, thanks to their particular properties.

35. Folkview views and authoring
In the

36. gure the presence of a black triangle symbol, in two
positions, correspond to selected/not selected : these triangles are
associated to scripts related to the session agent of the current
visualization, and represent the mean to interact with the
cell-agent.
When selected, the session agent asks to the chosen resource (r , 9
in our example) the set of actions Ac that can be activated on it.
Then r sends a multicast message to all the dimensions in
9
which it is included, and a run-time created contextual menu,
organized in three meta-categories (views, metrics and semantics)
is shown.
I The

37. rst category menu is concerning the dierent kinds of
possible views
I The other two categories of functions oered by the menu, are
related to:
I the computation of an extensible set of metrics ,
I the application of opportune semantic relations and ontologies
in order to generate, for example, speci

38. c recommendations on

39. Related works
I Few works have addressed the problem of interactive
visualizations of folksonomies:
I customized cluster maps for visualizing both the overview
and the detail of semantic relationships intrinsic in the
folksonomy (Montero et al., 2007);
I using information visualization techniques (IF) to discover
implicit relationships between users, tags and bookmarks,
end-users have dierent ways to discover content and
information otherwhise dicult to understand (Klerx and
Duval, 2009)
I the TagGraph project (http://taggraph.com/) is a folksonomy
navigator which visualizes the relationships between Flickr tags.
I Nevertheless these works do not provide neither
personalized views nor eective dynamic changes
according to the user needs or preferences.

40. Conclusion and future work
I We have proposed an innovative way to conceive a
folksonomy in terms of a multi-agent system,

41. rst de

42. ning a
formal model and then showing Folkview.
I We have built a partial, but modular and extensible,
prototype, based on a public dataset taken from delicious,
and that implements the structural aspects of the considered
folksonomy.
I As future work we want to extend the prototype:
I to all the main functionality we discussed, focusing our
attention on a semantic personalization;
I to extract data from a large number of social tagging systems.