=Paper=
{{Paper
|id=Vol-292/paper-9
|storemode=property
|title=Extreme Tagging: Emergent Semantics through the Tagging of Tags
|pdfUrl=https://ceur-ws.org/Vol-292/paper9.pdf
|volume=Vol-292
|authors=Vlad Tanasescu and Olga Streibel,pages 84-94
|dblpUrl=https://dblp.org/rec/conf/semweb/TanasescuS07
}}
==Extreme Tagging: Emergent Semantics through the Tagging of Tags==
https://ceur-ws.org/Vol-292/paper9.pdf
Extreme Tagging: Emergent Semantics through the
Tagging of Tags
Vlad Tanasescu1, Olga Streibel2
1
Knowledge Media Institute, The Open University,
Walton Hall, Milton Keynes, MK7 6AA, UK
v.tanasescu@open.ac.uk, vladtn@gmail.com
2
Netzbasierte Informationssysteme, Freie Universität Berlin,
14195 Berlin, Germany
streibel@inf.fu-berlin.de, ostreibel@gmail.com
Abstract. While the Semantic Web requires a large amount of structured
knowledge (triples) to allow machine reasoning, the acquisition of this knowl-
edge still represents an open issue. Indeed, expressing expert knowledge in a
given formalism is a tedious process. Less structured annotations such as tag-
ging have, however, proved immensely popular, whilst existing unstructured or
semi-structured collaborative knowledge bases such as Wikipedia have proven
to be useful and scalable. Both processes are often regulated through social
mechanisms such as wiki-like operations, recommendations, ratings, and col-
laborative games. To promote collaborative tagging as a means to acquire un-
structured as well as structured knowledge we introduce the notion of Extreme
Tagging, which describes systems which allow the tagging of resources, as well
as of tags themselves and their relations. We provide a formal description of ex-
treme tagging followed by examples and highlight the necessity of regulatory
processes which can be applied to it. We also present a prototype implementa-
tion.
Keywords: semantic web, web2.0, tagging, emergent semantics, meaning, se-
mantic associations, knowledge paths.
1 Introduction
The process of building “a new brain for humankind” [1] as foreseen by semantic web
research appears to be a slow one. Indeed, the semantic web contributed to the suc-
cess of the notion of ontology, “a logical theory accounting for the intended meaning
of a formal vocabulary, i.e. its ontological commitment to a particular conceptualisa-
tion of the world” [2], but, possibly due to lack of software support [3], ontologies are
difficult to build, even at the community level. Moreover, the final aim of the seman-
tic web – data integration through ontology matching – is still a research question as it
can be automated only in simple cases. Indeed, although there are already a large
number of RDF files on the web, whether manually or automatically generated, only
about 25 000 documents [4] representing semantic models, i.e. ontologies, are avail-
84 International Workshop on Emergent Semantics and Ontology Evolution
able online. This should not be the case, as ontologies should be easy to produce by
each community, then shared in order to be aligned with others using the stack of
specifications and languages – the semantic web “layer cake” [5] – designed to sup-
port this task [6].
In practice however, building and matching ontologies, appears to be an expert
task, and difficulties related to knowledge acquisition, experienced decades ago in the
artificial intelligence community, resurface. Moreover, while ontologies seem well
suited to the description of scientific domains such as medicine and biology which are
already semi-formal and organized by categories and part-of relationships, some
communities such as geospatial scientists only accept with scepticism the exclusive
usage of ontologies to describe their domains [7]. Arguments in favour of using alter-
native knowledge representation models include, amongst other, the inadequacy of
category based reasoning to represent reality [8], the absence of grounding of sym-
bolic systems [9], the need of different representations of the same entity according to
the context [10], as well as the difficulty to represent psychological concepts such as
affordances in a hierarchical way [11]. Indeed, we are still waiting for ontologies to
be flexible enough to match the representational complexity of the human mind.
In the meantime, so called Web2.0 applications, by motivating users to contribute
information, introducing fine tuned social regulation mechanism, as well as providing
friendly user interfaces, have been experiencing both phenomenal growth and suc-
cess. With the advent of Web2.0 the usage of unstructured annotations such as tag-
ging, spread widely. Although the relation of tagging and social interaction has not, to
our knowledge, been investigated in the literature, it seems to be the only way to al-
low users to describe their own content, since the system cannot determine in advance
what this content will be. Collaborative tagging systems, by renouncing the use of
predefined vocabularies, provide a simple way for users to give their own meaning to
their own content [12].
Therefore, while current research is still trying to alleviate problems related to the
practical use of ontologies, the semantic web may benefit from techniques used by
Web2.0 applications. We believe that for the semantic web to expand faster, new se-
mantic acquisition approaches, distinct from the centralized ontology development by
experts, need to be explored. We also believe that any successful solution will use the
social lever which raised the Web and Web2.0 to that level of popularity and usage.
Therefore, we introduce the notion of Extreme Tagging Systems (ETS), as an ex-
tension of collaborative tagging systems allowing the collaborative construction of
knowledge bases. An ETS offers a superset of the possibilities of collaborative tag-
ging systems in that they allow to collaboratively tag the tags themselves, as well as
relations between tags. Unlike previous research on emergent semantics of collabora-
tive tagging systems, ETS are not destined to exclusively produce hierarchical on-
tologies but strive to allow the expression and retrieval of multiple nuances of mean-
ing, or semantic associations. The production of relevant semantic associations can
then be automatically controlled through social network regulation mechanisms.
We first describe collaborative tagging systems. Then show the modifications in-
troduced by extreme tagging systems, providing a formal definition. Accordingly, we
explain our prototype implementation, and, before concluding, give some examples of
regulation mechanisms that should be applied to the system.
ESOE, Busan - Korea, November 2007 85
2 The Semantics of Collaborative Tagging Systems
Collaborative tagging systems (CTS) support multiple users in the activity of tagging,
which is marking content for future navigation, filtering or search [13]. As there is no
prior agreed structure or shared vocabulary CTS users need neither prior knowledge
nor specific skills to use the system [14]. We prefer to avoid the term folksonomy
[15], not only because it is ambiguous (as stated in [13]), but also because of the rela-
tion to taxonomy, which seems to us unjustified in that context.
Tagging systems can be represented as hypergraphs [16] where the set of vertices
is partitioned into sets:
U {u 1 ,. . ., u k } , R {r1 , ... , rm } , and T {t1 , . . . , t l } . (1)
U, R, and T correspond to users, resources, and tags. An annotation, i.e. a resource
tagged with a tag by a user, is an element of set A, where:
A U uR uT . (2)
The final hypergraph formed by a collaborative tagging system is defined as G
with:
G V , E with vertices V U R T , and edges (3)
E {{u, r, t} | (u, r, t) A} .
Collaborative tagging systems have proved extremely popular. Their strengths con-
sist in generating serendipity while browsing – the fact of being able to retrieve what
others have tagged in a similar way, e.g. one can retrieve everything that has been an-
notated using the tag “ant” –, as well as the elaboration of desire lines – a non con-
strained reflection of the user’s vocabulary – through a dataset [12] (e.g. I can use the
English tag “ant” or the French “fourmi” indifferently, without being constrained by
the system). However, when compared to more formal descriptions of domains, CTS
are criticized for their ambiguity (an “ant” tag may be found for a resource related to
“Actor Network Theory”, the “Apache Ant project”, or a representation of the insect),
the dealing with multiple words constituting a single tag (“semantic web”, “seman-
ticweb” or “semantic-web” for example) or synonymy (“mac” “macintosh”, and “ap-
ple”). These issues have leaded some to colloquially describe tagging systems as “a
mess”.
To go toward “less mess”, approaches have been proposed to find groups of related
tags by using tag co-occurrence for given resources [17][18][19]. Moreover, most
websites using collaborative tagging systems already present tag clouds – a represen-
tation of a resource’s annotations where each tag is visually weighted by his number
of occurrences –, or allow presentation by tag clusters – several tags are grouped un-
der an appellation – and often offer tag recommendations – tags are suggested accord-
ing to previous annotations.
Furthermore, some semantic web oriented approaches attempt to extract ontologies
from collaborative tagging systems. In [16] the author maps tags onto concepts and
resources to instances and applies network analysis techniques to cluster them. [20]
presents an ontology for tags which would allow them to be shared and exchanged be-
86 International Workshop on Emergent Semantics and Ontology Evolution
tween systems, while in [21] the authors mine association rules between tagged re-
sources to recommend tags, users, or resources, discovering supertag relations as well
as resource communities. In [22] the authors deduce clusters and relations between
tags by relating them to background knowledge obtained through ontology searches
while [14] presents an experiment to automate the previous method.
Ultimately, ontologies and tagging systems are both symbolic frameworks, and as
such they are subject to the criticism of the lack of a retrievable grounding. Indeed, in
both cases by using symbols (in a given language), the expressed concept, or signi-
fied, remains in people's minds, and the resulting symbol networks may appear – es-
pecially to a machine – as “free floating island[s] of reeds [with] no anchor in reality”
[9]. However, CTS usually tag existing resources, i.e. specifying the referent, or
ground, that symbols denote, without indicating the details of this denotation, as op-
posed to ontologies which first have to describe a domain before adding instances,
and limit the grounding to a few pre-existing relations, i.e. the ones defined in the on-
tology (e.g. “part-of”) plus the one assumed by the model (e.g. “is-a”, “subclass-of”,
etc). Extreme tagging, by allowing the tagging of tags as resources as well as the
specification of the relations between tags, is an attempt to push symbolic annotation
frameworks to an extreme in order to see what the grounding problem becomes when
any relation can be symbolically described at an arbitrary level of granularity.
3 Extreme Tagging Systems
An Extreme Tagging System (or ETS) offers a superset of the possibilities of collabo-
rative tagging systems in that it allows to collaboratively tag the tags themselves, as
well as relations between them. For example, a media resource representing the close-
up of a car may be tagged with “car”, “wheels” and “travel”. The tag “wheels” itself
may then be tagged (possibly by a different user) with “car” and “wheel”, and the tag
“car” itself could further be tagged with “vehicle” (cf. Figure 1)1.
Figure 1. Tagging the tags.
1 Picture from Flickr user Anjuli: http://www.flickr.com/photos/49502989227@N01/56641591/
ESOE, Busan - Korea, November 2007 87
The tagging of tags is justified by the fact that a tag can have different meanings in
different contexts: tagging the tags and the relations between them is used to disam-
biguate these contexts. For example the tag “tank” on the Flickr photo sharing ser-
vice2 is used to tag military vehicles3, fish tanks4 as well as a person5. Tag tagging al-
lows a user to explain the meaning of her or his annotations. It also reveals the
multiciplicity of meanings: by tagging “tank” with “fish” and “vehicle”, the ambigu-
ity becomes apparent and users can then decide to filter accordingly.
The operation of tagging introduces a relation which is not only functional (some-
thing has been tagged by somebody) but also a meaningful (for some reason, exclud-
ing spam, somebody tagged something with this particular tag). Indeed tagging a pic-
ture with “wheels” may relate to what the picture depicts, and a tag “travel” may
relate to the origin of the picture. However, the meaning of the relation is not made
explicit by the user at the moment of tagging: we believe that not having to think pre-
cisely to the relation and verbalise it as one would do in an ontology results in a
smaller cognitive load for the user and is part of the appeal of tagging. In extreme
tagging however, this relation itself can be tagged, later on, by any user. The opera-
tion of tagging relations between tags can naturally be expressed by triples, for exam-
ple, if “_” represents the implicit relation introduced by the tagging operation itself,
while “…” is used to represent any tag, relations can be: (resource, {_}, “wheels”),
(resource, {_}, “travel”), (resource, {_}, ...), (resource, {“shows”}, “wheels”), (re-
source, {“represents”}, “wheels”), (resource, {...}, “wheels”) or (resource, {“taken-
during”}, “travel”), etc. (cf. Figure 2)
Figure 2. Tagging relations between tags.
Allowing users to tag the tags and the relations between them leads to the genera-
tion of Semantic Associations. Semantic Associations are chains of relations between
one tag to another, or, in graph theoretic terms, a labelled path between two nodes.
According to the definitions of [23] and [24], two entities are semantically associated
2
Flickr, http://www.flickr.com/
3
e.g. http://www.flickr.com/photos/barryslemmings/tags/tank/
4
e.g. http://www.flickr.com/photos/towert7/tags/tank/
5 http://www.flickr.com/photos/50836387@N00/tags/tank/
88 International Workshop on Emergent Semantics and Ontology Evolution
if they are semantically connected, i.e. there exist a path of relations between them, or
semantically similar, i.e. two entities are similar if a path from the first one to another
is similar to the path from the second one to another. We also call semantic annota-
tions knowledge paths, as in this context they represent a crystallisation of the users’
knowledge. We consider that the tagging relation itself, even if implicit, qualifies as a
relation in a knowledge path, while we consider that the notion of semantic similarity
can be extended from subclass/superclass relations only to any similarity measure.
Collaboratively tagging resources, tags and relations leads to serendipitous discovery
of associations between resources and/or tags. An example path between “wheel” and
“vehicle” for example would be, expressed as a list of triples <“wheel”, “vehicle”> =
[(“wheel”, {“singular-of”}, “wheels”), (“wheels”, {_}, “car”), (“car”, {“is-a”}, “vehi-
cle”)].
The ETS model is defined as a collaborative tagging system with semantic associa-
tions. Therefore ETS are extensions of the formal model for collaborative tagging sys-
tems, defined as follows:
: U , T , A, D , where A U u T u T and D U u T u T u T . (4)
We do not distinguish between the set of resources/entities T and the set of tags: all
elements of T are entities, which can be “tags” or “resources”. Indeed the mapping
description of each entity by a unique identifier – in practice, a URI – makes the dis-
tinction superfluous. A is the set of assignments, as in traditional CTS while D repre-
sents directional annotations of relations between entities (tags or resources). Accord-
ing to this definition an ETS becomes a hypergraph:
G V , E , with vertices V U T , and edges (5)
E ^^u, r, t , d ` u, r, t A (u, r, t , d ) D` .
The distinction between A and D reflects the distinction between implicit and ex-
plicit relations. An implicit relation occurs when an entity has been tagged while an
explicit one appears if the relation between two entities has itself been tagged. A
knowledge path is a path consisting of explicit or implicit relations between entities.
As relations between tags constitute triples, the link to RDF becomes obvious. In-
deed ETS have the same goals as those sometime advocated by RDF proponents, “to
allow anyone to say anything about anything” [25]. However, if ETS triples can be
represented as RDF, extreme tagging introduces novelties. Indeed, RDF resources ac-
quire their unique identity through the use of namespaces which contributes to slow-
ing the process of knowledge acquisition as pre-existent knowledge about entities is
needed. For example in the context of fish tanks the entity “http://fish.com/#tank” is
needed, instead of “http://military.org/#tank”. In ETS however, a tag is tagged by all
its meanings, and disambiguation occurs during the query process, not at the tag de-
scription level, i.e. “tank” is only one tag, with a unique URI. If it is tagged as con-
tainer and as a weapon, disambiguation will occur during knowledge path elicitation,
as the knowledge path leading from “tank” to “fish” or “sea” will only use one of the
meanings.
ESOE, Busan - Korea, November 2007 89
4 Tagopedia: an Extreme Tagging System
Tagopedia6 is a prototype ETS built on top of the Facebook platform. Facebook is a
social network web application providing a developer framework allowing the crea-
tion of applications which interact with core host features such as profile management
and login. As any collaborative tagging system Tagopedia allows to tag resources,
represented by URIs (cf. Figure 3).
Figure 3. Basic Tagopedia usage.
When clicking on a tag however, the user is asked to tag the chosen relation or to
enforce an already existing relation by selecting it (cf. Figure 4). The application then
moves to the target tag, showing the linked tags and resources and allowing to define
new relations. The user may also choose not to tag the relation and directly reach the
target, keeping it implicit.
Figure 4. Tagging relations in Tagopedia.
Here is an example of collaboratively build semantic associations between entities
in Tagopedia, written as a list of triples:
sa1:
[(“John Boorman”, {directed}, “Excalibur”),
(“Excalibur”, {about}, holy-grail) ,
(holy-grail, {similar-to}, grail),
6
Available at http://apps.facebook.com/tagopedia/ (a Facebook account is required). The name
Tagopedia, proposed independently by the authors, has already been proposed in 2005 by
Russell Beattie in a blog post, for a related application
(http://www.russellbeattie.com/notebook/1008277.html).
90 International Workshop on Emergent Semantics and Ontology Evolution
(grail, {topic-of}, “http://www.imdb.com/title/tt0071853/”),
(“http://www.imdb.com/title/tt0071853/”, {has-title}, “Monty Python
and the Holy Grail”),
(“Monty Python and the Holy Grail”, {directed-by}, “Terry Gilliam”)]
sa2:
[(“John Boorman”, {is-a}, film-director), (film-director, {includes},
“Terry Gilliam”)]
sa3:
[(“John Boorman”, {is}, British), (British, {nationality-of}, “Terry Gilliam”)]
5 Emergent Semantics
In ETS, semantics are related to the users’ activity and input. The operations involved
in a user’s activity can be classified as: annotation, navigation and control. At each
level there is a need for incitation, a means to motivate the user to use the system. As
a result of these three operations, tags are created and annotated collaboratively and
unconstrained semantics emerge (cf. Figure 5). In this section, we describe each activ-
ity in turn as well as the corresponding motivation mechanism:
Figure 5. Plurality of meanings.
Through annotation users are given the opportunity to create their personal knowl-
edge base. Indeed, instead of tagging resources at their hosting websites, building un-
related islands of tags, they can relate all their resources with their own meaning.
Moreover, they can access, for the same resource, tags from other users, and decide to
explore their meaning by navigating to them.
Through navigation, users build or enforce semantic associations. Indeed, by ex-
ploring a tag which tags a tag, either the user is looking for an explanation of this tag,
or she already knows the relation. We assume that she knows the relation if a) she
tagged it before, or b) she chooses to tag it when asked to do so. As previously men-
tioned, navigation does not happen between a tag and another tag without presenting
the relation, which the user can choose to tag or not. The motivation of this additional
step is to constrain the meanings obtained. Paths which have been explored and vali-
dated, are recorded and displayed the next time a request is made to find the paths
from one node to another.
ESOE, Busan - Korea, November 2007 91
Finally control mechanisms are necessary in order for the system to evolve, some
of these control mechanisms can be:
1) total control over ones annotations: the annotations added by a user can be
modified or deleted by her.
2) appreciation and depreciation of tags: a user can rank a tag (+ or – only). If
the total ranking goes below a given threshold, the tag becomes “private” and
does not appear in public searches any more. A similar method is already used
by commercial websites7.
3) questions to author: Facebook, just as other social networks provides the no-
tion of “friends”, or “contacts”, i.e. users which acknowledged a mutual rela-
tionship. If a user does not understand a tagging made by one of her related
users a quick means is provided to send him or her a message to ask for an ex-
planation, i.e. the tagging of this particular relation. The requester is notified
as soon as the explanation has been given.
It is assumed that each user is interested in sharing his or her vision of the world
and in discovering other ways of perceiving it. To the first interest corresponds the
annotation activity as well as some control activities number 1) and 2). As a further
incitation annotating increases the user’s ranking, in a similar way as internet forums
display titles according to the number of posts (often quite imaginative, for example
using a graduated scale going from rookie, to half-god or absolute guru). In parallel,
an increase in status can be achieved through navigation only, in a similar manner to
some multiplayer computer games which increase the avatar’s status by providing ti-
tles according to the percentage of the virtual map explored. Indeed, These two ways
of using the system, annotating and creating, combine when a navigator – i.e. a user
who mostly navigates the system, comparable to a Wikipedia reader rather than to an
editor – earns creation points by completing paths and creators earn more points if the
paths they created are navigated (i.e. if they make sense). Further incitation may in-
volve visualisation of the number of elements created, as well as graph presentation
of the paths explored.
6 Conclusion and Future Work
The benefits of pushing tagging to an extreme are the ease with which knowledge is
acquired, as well as the comprehensiveness of the resulting KB. Possible caveats,
which we believe can be solved by collaborative means, include the difficulty to as-
sess the relevance of the resulting knowledge in a given context. Tagopedia is a first
prototype of an Extreme Tagging System and we are waiting to obtain a larger
knowledge base to attempt a serious evaluation. However, we used the prototype in a
limited environment composed of 5 users, and, from the amount of serendipitous
meaning collected, were already convinced of the interest of the system. We are plan-
ning to release it to the Facebook community in the following months and explore the
7 e.g. Spockcom, http://www.spock.com/.
92 International Workshop on Emergent Semantics and Ontology Evolution
aforementioned control mechanisms through it. We are also working on an RDF ex-
port mechanism as well as on the integration of a SPARQL query engine. We also
plan to import large amounts of tags from Wikipedia and other websites, using links
inside the pages or other structured information in order to populate the knowledge
base.
Acknowledgments. The authors are grateful to the anonymous reviewers for their
precious comments. They would also like to thank Yiwen Wang, Kaixuan Wang and
Fadi Badra, who contributed ideas and energy in the early stages of this project. Fur-
ther thanks are extended to Sean Bechhofer, Enrico Motta, and John Domingue, who
contributed, during SSSW07 and later on, to the success of this enterprise, and to
Lyndon Nixon for proofreading this paper.
References
1. D. Fensel and M. Musen, "The semantic web: a brain for humankind", Intelligent Systems,
vol. 16, pp. 24-25, 2001.
2. N. Guarino, Formal Ontology in Information Systems: Proceedings of the 1st International
Conference June 6-8, 1998, Trento, Italy. IOS Press, 1998.
3. M. Dzbor, E. Motta, C. Buil, J. Gomez, O. Görlitz & H. Lewen: Developing ontologies in
OWL: An observational study. OWL: Experiences & Directions Workshop, Georgia, US,
10-11 November 2006
4. M. d’Aquin, M. Sabou, M. Dzbor, C. Baldassarre, L. Gridinoc, S. Angeletou, and E. Motta.
WATSON: A Gateway for the Semantic Web. Poster session of the European Semantic
Web Conference, ESWC 2007.
5. The Semantic Web “Layer Cake”: http://www.w3.org/2004/Talks/0412-RDF-
functions/slide4-0.html
6. Sir Tim Berners-Lee at the Oxford Internet Institute, from webcast:
http://webcast.oii.ox.ac.uk/?view=Webcast&ID=20060314_139
7. W. Kuhn: Why Information Science needs Cognitive Semantics - and what it has to offer in
return, 2003.
8. A. Wierzbicka: “Apples” Are Not a “Kind of Fruit”: The Semantics of Human Categoriza-
tion American Ethnologist, JSTOR, 11, 313—328, 1984.
9. P. Gärdenfors: How to Make the Semantic Web More Semantic Formal Ontology in Infor-
mation Systems, Proceedings of the Third International Conference (FOIS), 17-34, 2004.
10. C. Vangenot, C. Parent and S. Spaccapietra: Modeling and manipulating multiple represen-
tations of spatial data Proc. of the Symposium on Geospatial Theory, Processing and Ap-
plications, 2002.
11. J. Gibson: The Ecological Approach to Visual Perception, Lawrence Erlbaum Associates,
1979, p.42-43
12. A. Mathes: Folksonomies-Cooperative Classification and Communication Through Shared
Metadata Computer Mediated Communication, LIS590CMC (Doctoral Seminar), Graduate
School of Library and Information Science, University of Illinois Urbana-Champaign, De-
cember, 2004
13. S. Golder & B. Huberman: The Structure of Collaborative Tagging Systems, Arxiv preprint
cs.DL/0508082, 2005
14. Angeletou, S., Sabou, M., Specia, L., Motta, E., (2007) Bridging the Gap Between Folkso-
nomies and the Semantic Web: An Experience Report. Workshop: Bridging the Gap be-
tween Semantic Web and Web 2.0, European Semantic Web Conference.
ESOE, Busan - Korea, November 2007 93
15. Vander Wal, T. Folksonomy, http://www.vanderwal.net/folksonomy.html, 2007
16. Mika, P. Ontologies are us: A unified model of social networks and semantics Proc.
ISWC2005, 2005
17. P. Schmitz. Inducing Ontology from Flickr Tags. In Proc. of the Collaborative Web Tag-
ging Workshop at WWW’06, 2006.
18. X. Wu, L. Zhang, and Y. Yu. Exploring Social Annotations for the Semantic Web. In Proc.
of WWW’06, 2006.
19. G. Begelman, P. Keller, and F. Smadja. Automated Tag Clustering: Improving search and
exploration in the tag space. In Proc. of the Collaborative Web Tagging Workshop at
WWW’06, 2006.
20. Gruber, T., Ontology of Folksonomy: A Mash-up of Apples and Oranges. AIS SIGSEMIS
Bulletin, 2005. 2 (3&4).
21. Schmitz, C.; Hotho, A.; Jaschke, R. & Stumme, G. Mining association rules in folksono-
mies Data Science and Classification: Proc. of the 10th IFCS Conf., Ljubljana, Slovenia,
July, Springer, 2006
22. L. Specia and E. Motta. Integrating Folksonomies with the Semantic Web. In Proc. of
ESWC’07, 2007.
23. A. Sheth et al., Semantic Association Identification and Knowledge Discovery for National
Security Applications. 2004.
24. K. Anyanwu and A. Sheth, “P-Queries: enabling querying for semantic associations on the
semantic web”, Proceedings of the 12th international conference on World Wide Web, pp.
690-699, 2003.
25. I. Davis, Introduction to RDF slides, http://research.talis.com/2005/rdf-intro/#(7), 2005
94 International Workshop on Emergent Semantics and Ontology Evolution