=Paper=
{{Paper
|id=Vol-292/paper-11
|storemode=property
|title=Understanding the Semantics of Ambiguous Tags in Folksonomies
|pdfUrl=https://ceur-ws.org/Vol-292/paper11.pdf
|volume=Vol-292
|authors=Ching-man Au Yeung,Nicholas Gibbins,and Nigel Shadbolt,pages 108-121
|dblpUrl=https://dblp.org/rec/conf/semweb/YeungGS07a
}}
==Understanding the Semantics of Ambiguous Tags in Folksonomies==
https://ceur-ws.org/Vol-292/paper11.pdf
Understanding the Semantics of Ambiguous
Tags in Folksonomies
Ching-man Au Yeung, Nicholas Gibbins, and Nigel Shadbolt
Intelligence, Agents and Multimedia Group (IAM),
School of Electronics and Computer Science,
University of Southampton,
Southampton SO17 1BJ, UK
{cmay06r,nmg,nrs}@ecs.soton.ac.uk
Abstract. The use of tags to describe Web resources in a collaborative
manner has experienced rising popularity among Web users in recent
years. The product of such activity is given the name folksonomy, which
can be considered as a scheme of organizing information in the users’ own
way. In this paper, we present a possible way to analyze the tripartite
graphs – graphs involving users, tags and resources – of folksonomies and
discuss how these elements acquire their meanings through their associ-
ations with other elements, a process we call mutual contextualization.
In particular, we demonstrate how different meanings of ambiguous tags
can be discovered through such analysis of the tripartite graph by study-
ing the tag sf. We also discuss how the result can be used as a basis to
better understand the nature of folksonomies.
1 Introduction
The use of freely-chosen words or phrases called tags to classify Web resources
has experienced rising popularity among Web users in recent years. Through the
use of tags, Web users come to share and organize their favourite Web resources
in different social tagging systems, such as del.icio.us1 and Flickr2 . The result of
this collaborative and social tagging activity is given the name folksonomy, which
refers to the classification system evolved from the individual contributions of
tags from the users [1].
Collaborative tagging possesses a number of advantages which account for
its popularity. These include its simplicity as well as the freedom enjoyed by the
users to choose their own tags. However, some limitations and shortcomings, such
as the problem of ambiguous meanings of tags and the existence of synonyms,
also affect its effectiveness to organize resources on the Web. As collaborative
tagging attracts the attentions of researchers, methods on how useful information
can be discovered from the seemingly chaotic folksonomies have been developed.
In particular, some focus on discovering similar documents or communities of
1
http://del.icio.us/
2
http://www.flickr.com/
108 International Workshop on Emergent Semantics and Ontology Evolution
shared interests [17, 13], while some perform analysis on the affiliation between
entities to find out different relations between tags [10, 14].
In this paper we focus on analysis of tripartite graphs of folksonomies, graphs
which involve the three basic elements of collaborative tagging, namely users,
tags and resources. We present how these elements come to acquire their own
semantics through their connections with other elements in the graphs, a process
which we call mutual contextualization. In particular, we carry out a preliminary
study on tripartite graphs with data obtained from del.icio.us, and demonstrate
how we can understand the semantics of ambiguous tags by examining the struc-
tures of these graphs. We also discuss how the result can be used as a basis to
acquire a better understanding of the nature of folksonomies.
The rest of this paper is structured as follows. Section 2 gives some back-
ground information on collaborative tagging systems and folksonomies. We de-
scribe the process of mutual contextualization between the three basic elements
in Section 3. We detail the preliminary study on tripartite graphs of folksonomies
in Section 4, followed by discussions in Section 5. Finally we present our conclu-
sions and discuss possible future research directions in Section 6.
2 Background
2.1 Collaborative Tagging Systems
Tagging originates from the idea of using keywords to describe and classify re-
sources. These keywords are descriptive terms which indicate the topics ad-
dressed by the resources. Collaborative tagging systems emerged in recent years
have taken this idea further by allowing general users to assign tags, which are
freely-chosen keywords, to resources on the Web. For example, one can store
a bookmark of the page “http://www.google.com/” on a collaborative tagging
system, and assign to it the tags google, search and useful. As the tags of different
users are aggregated, the tags form a kind of signature of the document, which
can be used for future retrieval or indication of the nature of the page.
Collaborative tagging systems have started to thrive and grow in number
since late 2003 and early 2004 [6]. As one of the earliest initiative of collaborative
tagging, del.icio.us provides a kind of social bookmarking service, which allows
users to store their bookmarks on the Web, and use tags to describe them.
Other services focusing on different forms of Web resources appeared shortly.
For example, Flickr allows users to tag digital photos uploaded by themselves.
Collaborative tagging are generally considered to have a number of advan-
tages over traditional methods of organizing information, as evidently shown
by its popularity among general Web users and its application on a wide range
of Web resources. The following features of collaborative tagging are generally
attributed to their success and popularity [1, 15, 18].
Low cognitive cost and entry barriers The simplicity of tagging allows any Web
user to classify their favourite Web resources by using keywords that are not
constrained by predefined vocabularies.
ESOE, Busan - Korea, November 2007 109
Immediate feedback and communication Tag suggestions in collaborative tagging
systems provide mechanisms for users to communicate implicitly with each other
through tag suggestions to describe resources on the Web.
Quick Adaptation to Changes in Vocabulary The freedom provided by tagging
allows fast response to changes in the use of language and the emergency of new
words. Terms like AJAX, Web2.0, ontologies and social network can be used
readily by the users without the need to modify any pre-defined schemes.
Individual needs and formation of organization Tagging systems provide a con-
venient means for Web users to organize their favourite Web resources. Besides,
as the systems develop, users are able to discover other people who are also in-
terested in similar items.
On the other hand, limitations and problems of existing collaborative tagging
systems have also been identified [1, 13, 18]. These issues hinder the growth or
affect the usefulness of the systems.
Tag Ambiguity Since vocabulary is uncontrolled in collaborative tagging systems,
there is no way to make sure that a tag is corresponding to a single and well-
defined concept. For an example, items being tagged by the term sf may either
be related to something about science fiction or the city San Francisco.
The use of multiple words and spaces Some systems allow users to input tags
separated by spaces. Problems arise when users would like to use phrases with
multiple words to describe the Web resources.
The problem of synonyms Different tags can be used to refer to the same concept
in a tagging system. For example, “mac,” “macintosh,” and “apple” can all be
used to describe Web resources related to Apple Macintosh computers[1]. The
use of different word forms such as plurals and parts of speech also exacerbate
the problem.
Lack of semantics A tag provides limited information about the documents
being tagged. For example, when tagging an URL with the tag “podcast,” one
can mean that the website provides podcast, describes the use of podcast, or
provides details on the history of podcasting.
2.2 Folksonomies
As more tags are contributed to a collaborative tagging system by the users,
a form of classification scheme will take shape. Such scheme emerges from the
collective efforts of the participating users, reflecting their own viewpoints on
how the shared resources on the Web should be described using various tags.
This product of collaborative tagging is now commonly referred to as folksonomy
[16]. A folksonomy is generally agreed to be consisting of at least the following
three sets of entities [9, 10, 18].
110 International Workshop on Emergent Semantics and Ontology Evolution
Users Users are the ones who assign tags to Web resources in social tagging
systems. They are also referred to as actors, as in social network analysis.
Tags Tags are keywords chosen by users to describe and categorize resources.
Depending on systems, tags can be a single word, a phase or a combination of
symbols and alphabets. Tags are referred to as concepts in some works [10].
Resources Resources refer to the objects that are being tagged by the users in
the social tagging systems. Depending on the system, resources can be used to
refer to Web pages (bookmarks) as in del.icio.us or photos as in Flickr. Resources
are also referred to as instances, objects or documents, depending on the context.
Quite a number of research works perform analysis on social tagging systems.
However, even though most works adopt a model involving the above three
entities, with a few mentioning extra dimensions such as the time of tagging,
there is actually not a common consensus on the formal definition of folksonomy.
Below we summarize the attempts in this respect.
Mika [10] represents a social tagging system as a tripartite graph, in which
the set of vertices can be partitioned into three disjoint sets A, C and I, corre-
sponding to the set of actors, the set of concepts and the set of objects being
tagged. A folksonomy is then defined by a set of annotations T ⊆ A × C × I,
an element of which is a triple representing an actor assigning a concept to an
object being tagged.
Gruber [5] proposes a “tag ontology” which formalizes the activity of tag-
ging through the use of an ontology. He suggests that tagging can be defined
using a five-place relation: T agging(object, tag, tagger, source, [+/−]), with ob-
ject being the Web resources being tagged, tagger being the user who assigns
tags, source being the system from which this annotation originates, and [+/−]
representing either a positive or negative vote placed on this annotation by the
tagger. Newman [12] also developed a similar ontology for tagging. The act of
tagging is modelled as a relation T (Resource, T agging(T ag, Agent, T ime)).
Hotho et al. [7] define a folksonomy as a tuple F := (U, T, R, Y, ≺). The
finite sets U , T and R correspond to the set of users, tags and resources respec-
tively. Y refers to the tag assignments, which are ternary relation between the
above three sets: Y ⊆ U × T × R. ≺ is a user-specific relation which defines the
sub/superordinate relations between tags. By dropping ≺, the folksonomy can
be reduced to a tripartite graph, which is equivalent to Mika’s model.
3 Mutual Contextualization in Folksonomies
The power of folksonomies lies in the interrelations between the three elements.
A tag is only a symbol if it is not assigned to some Web resources. A tag is also
ambiguous without a user’s own interpretation of its meaning. Similarly, a user,
though identified by its username, is characterized by the tags it uses and the
resources it tags. Finally, a document is given semantics because tags act as a
ESOE, Busan - Korea, November 2007 111
form of metadata annotation. Hence, it is obvious that each of these elements in
a folksonomy would be meaningless, or at least ambiguous in meaning, if they are
considered independently. In other words, the semantics of one element depends
on the context given by the other two, or all, elements that are related to it.
To further understand this kind of mutual contextualization, we examine each
of the three elements in a folksonomy in detail. For more specific discussions, we
assume that the Web resources involved are all Web documents. In addition, we
define the data in a social tagging system, a folksonomy, as follows.
Definition 1. A folksonomy F is a tuple F = (U, T, D, A), where U is a set of
users, T is a set of tags, D is a set of Web documents, and A ⊆ U × T × D is
a set of annotations.
By adopting this definition, we are actually using the model described by Mika
[10]. Since we are mainly focusing on the associations between the three elements
and are obtaining data from a single social bookmarking site, information such
as the time stamps and sources of tagging is irrelevant here. Thus, the definition
we used here is a simple but sufficient one for our work presented here.
As we have mentioned, the three elements forming the tripartite graph of a
social tagging system are users, tags and documents (resources). The tripartite
graph can be reduced into a bipartite graph if, for example, we focus on a
particular tag and extract only the users and documents associated with it.
Since there are three types of elements, there can be three different types of
bipartite graphs. This step is similar to the method introduced by Mika [10].
However, we distinguish our method from that presented by Mika by focusing
on only one instance of a type (e.g. tags), instead of all the items of the same
type, allowing us to acquire more specific understanding of the semantics of the
instance.
3.1 Users
By focusing on a single user u, we obtain a bipartite graph T Du defined as
follows:
T Du = T ∪ D, Etd , Etd = {{t, d}|(u, t, d) ∈ A}
In other words, an edge exists between a tag and a document if the user has
assigned the tag to the document. The graph can be represented in matrix form,
which we denote as X = {xij }, xij = 1 if there is an edge connecting ti and dj .
The bipartite graph represented by the matrix can be folded into two one-mode
networks [10]. We denote one of them as P = XX , and another as R = X X.
P represents a kind of semantic network which shows the associations be-
tween different tags. It should be note that this is unlike the lightweight ontology
mentioned in [10], as it only involves tags used by a single user. In other words,
this is the personal vocabulary, a personomy [7], of a particular user.
The matrix R represents the personal repository of the user. Links between
documents are weighted by the number of tags that have been assigned to both
documents. Thus, documents having higher weights on the links between them
are those that are considered by the particular user as more related.
112 International Workshop on Emergent Semantics and Ontology Evolution
3.2 Tags
By using a similar method as described above, we can obtain a bipartite graph
U Dt regarding to a particular tag t:
U Dt = U ∪ D, Eud , Eud = {{u, d}|(u, t, d) ∈ A}
In words, an edge exists between a user and a document if the user has assigned
the tag t to the document. The graph can once again be represented in matrix
form, which we denote as Y = {yij }, yij = 1 if there is an edge connecting ui
and dj . This bipartite graph can be folded into two one-mode networks, which
we denote as S = YY , and C = Y Y.
The matrix S shows the affiliation between the users who have used the tag
t, weighted by the number of documents to which they have both assigned the
tag. Since a tag can be used to represent different concepts (such as sf for San
Francisco or Science Fiction), and a document provides the necessary content
to identify the contextual meaning of the tag, this network is likely to connect
users who use the tag for the same meaning.
C can be considered as another angle of viewing the issue of polysemous
or homonymous tags. Thus, with the edges weighted by the number of users
who have assigned tag t to both documents, this network is likely to connect
documents which are related to the same sense of the given tag.
3.3 Documents
Finally, a bipartite graph U Td can also be obtained by considering a particular
document d. The graph is defined as follows:
U Td = U ∪ T, Eut , Eut = {{u, t}|(u, t, d) ∈ A}
In words, an edge exists between a user and a tag if the user has assigned the
tag to the document d. The graph can be represented in matrix form, which we
denote as Z = {zij }, zij = 1 if there is an edge connecting ui and tj . Like in the
cases of a single user and a single tag, this bipartite graph can be folded into
two one-mode networks, which we denote as M = ZZ , and V = Z Z.
The matrix M represent a network in which users are connected based on
the documents commonly tagged by them. Since a document may provide more
than one kind of information, and users do not interpret the content from a single
perspective, the tags assigned by different users will be different, although tags
related to the main theme of the document are likely to be used by most users.
Hence, users linked to each other by edges of higher weights in this network
are more likely to share a common perspective, or are more likely to concern a
particular piece of information provided by the document.
On the other hand, the matrix V represents a network in which tags are
connected and weighted by the number of users who have assigned them to the
document. Hence, the network is likely to reveal the different perspective of the
users from which they interpret the content of the document.
ESOE, Busan - Korea, November 2007 113
We can see that different relations between the users, the tags and the docu-
ments in a folksonomy will affect how a single user, tag or document is interpreted
in the system. Each of these elements provide an appropriate context such that
the semantics of the elements can be understood without ambiguity.
4 Semantics of Ambiguous Tags
One problem in the existing collaborative tagging system is the existence of
ambiguous tags. By “ambiguous tags,” we refer to tags that are intended to
represent different concepts by the users. For example, in del.icio.us the tag sf
has been used to describe documents which are related to science fiction and
San Francisco. Another example is the tag opera, which are used for describing
contents related to opera as a kind of musical performance as well as those
related to the WWW browser which is named “Opera.”3
As we have discussed, the semantics of a tag depends on the context given by
the users who have used it as well as the documents being tagged. By studying
the associations between the tag, the users and the documents, we may determine
the different meanings of a tag by placing it in the right context. As an illustrative
example, we present an analysis of the bipartite graphs obtained from a single
tag, which we have chosen for its common occurrence and multiple equally-
frequent meanings in order to preserve the clarity of the example. In particular,
we would like to find out if it is possible to disambiguate a tag by studying its
association with different users and documents.
4.1 Understanding a Single Tag
In the experiment described below, we try to examine the networks of users and
documents associated with the tag sf, and attempt to understand how different
interpretation of the tag can be discovered from the analysis of the networks.
The reasons of choosing the tag sf as an illustrating example are twofold.
Firstly, sf is a tag used very frequently by users in del.icio.us. Although the exact
number of times that the tag has been used cannot be known from the system,
we are able to collect over 5000 triples which involves the tag sf. Secondly, by
observation, the tag sf has been used by users to refer to two very distinctive
concepts, namely “science fiction” and “San Francisco.” We expect that users
using the tag to refer to one of the two concepts do not use it to refer to the
other one. Hence, the tag sf is more worthwhile to be examined, and we expect
that experiments on the tag can produce clearer results for performing analysis.
In March 2007, data was collected from the del.icio.us website by using a
crawler program written in Python. The program retrieved pages listing all book-
marks that have been tagged with sf, and subsequently retrieved the published
RSS file of each bookmark to obtain the corresponding users and tags associated
with it. In other words, the crawler retrieved bookmarks in del.icio.us which have
3
http://www.opera.com/
114 International Workshop on Emergent Semantics and Ontology Evolution
Fig. 1. A network of documents tagged by sf.
been tagged with sf, along with the users who tagged the page, and the tags,
including sf, they used. In total, 238,117 triples were obtained, each involving a
user, an URL of the bookmark, and a tag. A total of 427 distinctive URLs and
19979 users are involved. Out of these triples, 5852 involves the tag sf.
We extract all those triples that involve the tag sf, and construct the matrix
Y, representing the associations between users and bookmarks (documents). We
then construct the matrices S = YY , corresponding to the network of users,
and C = Y Y, corresponding to the network of documents.
The matrices S and C are feeded into the network analysis package Pajek
[3], and visualized as networks. Since some users do not have any associations
with other users, as in the case of documents, isolated nodes are removed from
the networks. The results are shown in Fig 1 and Fig 2. In Fig 1, nodes represent
documents, and two nodes are connected by an edge if a user has tagged both
documents with the tag sf. Edges are weighted by the number of such users,
and is not shown in the figure. In Fig 2, nodes represent users, and two nodes
are connected by an edge if both users have tagged a document with the tag
sf. Edges are weighted by the number of such documents. The networks are
visualized using the Kamada-Kawai layout algorithm [8] implemented in Pajek.
Two large clusters of nodes can be observed in both of the networks in Fig 1
and Fig 2. However, as shown in the two figures, there are more connections
between the two clusters in the network of documents than in that of users. One
hypothesis that can be used to explain the existence of clusters in the network
of documents is that they correspond to groups of documents related to the
different senses of the tag sf. A similar hypothesis that can be applied to the
network of users is that the different clusters corresponds to groups of users who
have used the tag sf to represent different concepts.
Since documents are connected if a user tagged them with the tag sf, it
implies that connected documents are considered by the user as all related to
certain concept represented by the tag sf. In addition, if we assume that a user
ESOE, Busan - Korea, November 2007 115
Fig. 2. The network of users who used the tag sf.
would be consistent in using the same tag for the same concept, it is reasonable
to suggest that documents in different clusters would address a different concept
represented by the tag sf. As we understand through observation that two major
concepts – “science fiction” and “San Francisco” are associated with the tag sf,
we can further suggest that the two major clusters in the network correspond
to documents on science fiction and San Francisco respectively. To testify this
hypothesis, we perform further analysis on the tagging data.
Firstly, we manually examine all the 357 websites represented by the nodes in
the network of documents. We classify the websites into either related to science
fiction or San Francisco, based on the content of the website as well as other tags
used by the users. We indicate that the website cannot be classified into either
of these categories if not enough information or evidence is available. After that,
we combine the information with the original network, and use Pajek to draw a
new network, as shown in Fig 3.
In the figure, circular nodes represent documents related to science fictions,
and triangular nodes represent documents related to San Francisco. Documents
that cannot be classified are represented by rectangular nodes. We can see that
these two types of nodes are clearly grouped into two clusters. The result shows
that the two clusters indeed correspond to two sets of documents related to two
distinctive meaning of the tag sf.
However, it is interesting to note that there are actually a lot of edges con-
necting nodes from different clusters. Since nodes are connected if a user tagged
them with the tag sf, these connections imply that some users actually used the
same tag to represent two distinctive concepts. This also explains why the two
clusters in the network of users are connected by a few edges. The documents
connected by edges between clusters in the network of documents are then re-
sponsible for the edges connecting the users from different clusters in the network
of users. However, since it would be very difficult to judge accurately whether
116 International Workshop on Emergent Semantics and Ontology Evolution
Fig. 3. The network of documents tagged by sf with classified nodes.
a user always uses the tag sf to refer to science fictions or San Francisco, we
refrain from performing a similar classification of the users.
To further investigate whether there are many users who actually used the tag
to refer to more than one concept, we construct one more network of documents.
Based on the data which generates Fig 3, we remove edges which has a weight
less than 2. By doing that we effectively ignore all the edges which correspond
to cases in which only one user has used the tag sf on both of the documents
connected by an edge. We also remove nodes that are not connected to any other
nodes afterwards. The result is shown in Fig 4. it can be seen that there remains
only one edge which connects nodes across the two clusters.
Finally, we examine how different tags are associated with each other given
this set of documents and users. Since the documents are all tagged by the tag sf,
all the other tags can be considered to be related to it. Given the two distinctive
concepts represented by the tag, it is reasonable to hypothesize that the tags
related to it can also be divided into two groups, one being related to science
fictions, and another to San Francisco. We construct a matrix T = {tij } to
represent the associations between the tags. tij is the number of times tagi and
tagj have been used on the same document. Since there are over 8000 unique
tags in the data, and many of them have been only used on a few documents,
we only concentrate on 35 tags which are used most frequently along with sf.
The associations between the tags are visualized in Fig 5. We can see that
tags which are related to San Francisco are grouped in one cluster while tags
related to science fictions are grouped in another cluster. This suggests that we
can examine the related tags in order to obtain the different meanings of an
ambiguous tag.
ESOE, Busan - Korea, November 2007 117
Fig. 4. The network of documents tagged by sf after removal of edges with weights
less than two and unconnected nodes.
Fig. 5. The network of 35 tags which are most frequently used along with sf.
5 Discussions
The experiment results show that by analyzing the tripartite graph of folksonomy
and the relations between tags, users and documents, we can discover how tags
are being used, and better understand the meanings of the tags which are used
for multiple meanings. Hence, although the same tag can be used to represent
different concepts, the documents and the users still provide the context for
understanding specific meanings of the tag. Given the above results, we come to
understand more about the characteristics of folksonomies.
5.1 Ambiguous Tags from Users’ Point of View
Based on the facts that documents of similar topics are clustered together, and
that documents are connected by users who have applied the tag sf, we see that
118 International Workshop on Emergent Semantics and Ontology Evolution
the majority of users use the tag to refer to one concept only. This is because if
users use the tag arbitrarily to refer to any of the two concepts, we would not be
able to observe two clusters in the network. Hence, although a tag can possess
several distinctive meanings, users tend to be consistent in referring to the same
meaning when they use the tag. One may also suggest that users interested in one
concept represented by the tag are not interested in the other, thus producing
the two clusters of documents. However, given that the different senses of the
tags we examined do not actually have conflicts with each other, and that the
experiments actually involves quite a large number of users, it is more reasonable
to suggest that consistence in usage is the reason of the clear distinction that we
have observed. Hence, this shows that it is possible to understand whether a tag
has multiple senses by examining the associations between users and documents.
5.2 Existence of Sub-communities
In the experiment, in addition to the two large clusters of nodes, we can also
observe within the clusters that there are some nodes which tend to be grouped
with each other to form smaller clusters. For example, in Fig 3 on the left and
right ends of the clusters of triangular nodes, we can observe that some nodes are
more connected with each other than with the rest of the nodes. This is probably
because even if we consider all documents that are related to “San Francisco,”
there are still actually a wide range of documents related to different aspects of
“San Francisco.” If we look at the network of tags, we can see that tags related
to “San Francisco” include food, travel and culture. Thus, these smaller clusters
probably correspond to documents with more specific topics. More analysis will
be performed in the future to verify this hypothesis.
5.3 Identifying the Topics of Documents
There are some documents (rectangular nodes in the network) which we cannot
classify them into either the category of “science fiction” or “San Francisco.”
This is because either the documents are only very loosely related to one of these
topics, or the tags associated with it are not indicative enough. However, as these
rectangular nodes are located in one of the clusters we have observed, it becomes
possible to judge, with high probability, the topics of these documents. Also,
folksonomies reflect the classification scheme evolving from the collaborative
effort of users. Hence, this judgement is not necessarily aligned with the intention
of the author of the document. Rather, by saying that a document is related to
a certain topic as judged by its location in the network, we are reflecting the
opinions of the users. Thus, by constructing and examining the networks of
documents, we are able to place the documents into the appropriate context,
allowing us to understand what it is about from the viewpoint of users.
5.4 Related Works
Research on folksonomies mainly focuses on relations between tags instead of
the semantics of individual tags. For example, Begelman et al. [2] propose an
ESOE, Busan - Korea, November 2007 119
automatic tag clustering algorithm to tackle the problem of synonyms. A more
comprehensive method proposed by [14] is able to discover four different kinds
of relations – relevant, conflicting, synonymous and unrelated – between tags.
Mika [10] proposes to generate lightweight ontologies which are more meaning-
ful by examining tag relations in the social context instead of studying their
co-occurrences in documents. One piece of work which is closely related to topic
presented here is that by Wu et al. [18], in which the authors investigate how
emergent semantics can be derived from folksonomies. They employ statistical
analysis on folksonomies, and study the conditional probabilities of tags in dif-
ferent conceptual dimensions. Tags with multiple meanings will then score high
in more than one dimensions in the conceptual space. However, one limitation of
their method is that the number of dimensions must be determined beforehand.
6 Conclusions and Future Work
Our study shows that mutual contextualization does occur among the three basic
elements in a folksonomy, and that it is possible to acquire a better understanding
of the semantics of ambiguous tags by constructing and studying the networks
of documents and users associated with the tag.
Currently, many research works focus on how tagging data in folksonomies
can be utilized to provide other services, such as identifying user interests, recom-
mending relevant documents or constructing light-weight ontologies. However,
all these applications require a better understanding of the semantics of tags
in order to provide accurate and useful results. For example, it would not be
wise to match users based on the tags they used without knowing that tags may
possess different meanings. Hence, the work presented here can be considered as
a first step to acquire a better understanding of folksonomies.
However, challenge remains in that while we can identify different groups of
users and documents which correspond to different usage of an ambiguous tag,
we still need other methods to integrate these different pieces of information
to acquire the full picture. For example, how can we know, without examining
every documents, which groups of users and documents are associated with a
particular sense of a tag? This will be further investigated in our future work.
Specifically, in the future we will apply our method on other ambiguous tags
to observe its performance. We hope to gain more insight on how to devise some
automatic algorithms to perform tag meaning disambiguation. We will also study
different methods of hierarchical clustering or community-discovering algorithms
[4, 11], and investigate how these techniques can be applied to discover clusters
of documents and users. It is hope that, by further examining the tags associated
with different clusters, we can discover the different senses of a tag, probably by
examining the tags being used most frequently in the clusters. Finally, we will
extend our study to users as well as documents, and investigate how analysis
on tripartite graphs can help discover useful information such as communities of
users or clusters of documents with similar topics, which will be very useful in
applications such as Web page recommendation or social network analysis.
120 International Workshop on Emergent Semantics and Ontology Evolution
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