=Paper=
{{Paper
|id=Vol-181/paper-6
|storemode=property
|title=The Semantics of Collaborative Tagging System
|pdfUrl=https://ceur-ws.org/Vol-181/paper6.pdf
|volume=Vol-181
}}
==The Semantics of Collaborative Tagging System==
https://ceur-ws.org/Vol-181/paper6.pdf
The Semantics of Collaborative Tagging System
Milorad Tošić and Valentina Milićević
University of Niš,
Faculty of Electronic Engineering,
Intelligent Information Systems Lab – InfosysLab,
Ul. Aleksandra Medvedeva 14, PoBox 73, 18000 Nis,
Serbia and Montenegro
mbtosic@yahoo.com , valentina@elfak.ni.ac.yu
Abstract. In this paper, we adopt a system-oriented approach to the collabora-
tive tagging and define it as a set of interactions in the system of Web re-
sources. First, the system of Web resources is modeled as a set of interacting
agents and collaborative tagging is represented as concurrent initiation of inter-
actions between agents in the system. Also, we define concept of knowledge for
individual agents. Later we use concepts of interaction and knowledge to give
definition of a Link. Then, for a given Universal Set of Resources, we introduce
Tag Cloud System (TCS) and definition of (possibly fuzzy) collections of re-
sources. Finally, we introduce concept of Class, based on projection of collec-
tions of resources in the TCS, to lay down some of the groundwork towards
TCS-based type system.
Introduction
In its essence, Web is all about resource locators (URLs), resource identifiers
(URIs) and resource names (URNs) [1] distilling resource as one of the most funda-
mental concepts of the Web. Until recently, Web was considered only within its
original hyper-text framework: web pages are network retrievable text documents,
easy to render for human visual consumption, that may contain hyper-links to other
web pages. However, massive adoption of the Internet and particularly broadband
“last mile”, have changed the very nature of the Web that has now been declared
“Web as platform”. So, Web is not anymore for human eyes only but it is also Web of
data. Two different technological and philosophical methodologies are the most visi-
ble now days: Semantic Web [2] and Web 2.0 [3]. In spite of the impression that
some tension exists between these two communities, we consider Semantic Web and
Web 2.0 as two sides of the same coin addressing the same gap between how current
technology is applied and the new opportunities. The difference is in the philosophy –
general vs. simple: Semantic Web is based on a firm theoretical background and pur-
sues a rigorous, generic top-down approach. In the same time, Web 2.0 is extremely
flexible, based on ultimately simple, easy to use and easy to understand stuff, adopts
bottom-up approach and worships architecture of participation (services get better as
the number of users increases), collective intelligence and long tail model [4].
2 Milorad Tošić and Valentina Milićević
In [5], authors are concerned with knowledge acquisition for software develop-
ment, and accordingly they define tagging as chinking and indexing knowledge ac-
quisition dialogue using structures that are relevant to software development. How-
ever, the collaborative tagging is more traditionally considered within a framework of
strategies that can be used in order to classify and organize content [4,6,7]. The classi-
fication strategies are characterized by several distinguishing attributes: If each item
may be associated to exactly one category then the strategy is exclusive. If each cate-
gory belongs to a more general one until the root of the tree is attained then the strat-
egy is hierarchical. Strategy that is exclusive and hierarchical is called taxonomy.
One of the typical examples of the taxonomy is the hierarchical directory set up by
Yahoo Inc. as an impressive attempt to grow a kind of universal Web taxonomy. Tag-
ging system is a non-hierarchical and non-exclusive strategy where each item is being
assigned a list of keywords, called tags. All the tags are at the same level. The tagging
systems are further classified by means of who defines the set of words or phrases that
may be used as tags and who assigns tags to items. The set of tags may be defined by
a central authority, such as editor or a librarian, or may include any word composed of
letters. Tags may be assigned to items by the same central authority or by community.
For example, in the ACM Computing Classification System [8], the central authority
defines the set of keywords that may be used to classify a paper while author of the
paper assigns selected keywords to the paper1. Collaborative Tagging (Folksonomy)
is an ad hoc classification scheme that Web users invent as they surf to categorize the
data they find online. Consequently, it is anarchic (the choice for the keywords are
not restrained by any central authority but may be any string of alphanumeric charac-
ters) and democratic (the tagging is performed by a large ensamble of people, and not
by a central one) [7]. Social software – software that enables users to share informa-
tion and collaborate online – makes these tags available to other users, who can than
take advantage of all this tagging to search for the information they need [4]. This ap-
proach has become increasingly popular, and some Web sites (call them Web 2.0 or
not?) maintain tag cloud, a list of all tags used on the domain usually with a visual in-
dication of individual tag’s popularity. The collaborative filtering is a democratic
method of classification that does not require tags to be words only. The collaborative
filtering exploits user access patterns to link items to people who use it [7].
We can identify three orthogonal dimensions of the concept of scripting language:
1) Language characteristics that identify a programming language as a scripting lan-
guage (weak typing or even no typing at all, reflection and introspection, etc.); 2) Sys-
tem that is programmed by the scripting language (in the case of OS shell scripting
languages the system is set of OS commands, while in the case of MSVisualBasic the
system is composed of a set of registered ActiveX and/or COM components); and 3)
Application under development. In this paper, we are focused on theoretical founda-
tions for the second aspect, i.e. we envision Tag Cloud System as a system that will
1 To the best of our knowledge, there is no tagging system like ACM CCS where set of possible
keywords is defined by a central authority while readers assign the list of tags (or at least are
allowed to edit it) to the paper instead of author of the paper.
The Semantics of Collaborative Tagging System 3
be programmed by future semantic scripting languages in order to develop whole new
set of global scalable applications for the “Web as a platform”. Introduction of seman-
tics into the traditional scripting brings in two additional levels of freedom: 1) Using
existing scripting languages to develop semantic applications (e.g. JavaScript pro-
grams a client side while Ruby on Rails, PHP and scripting language for program-
ming plug-ins in Wiki are on a server side), and 2) Using “semantic scripting lan-
guage” to develop not exclusively semantic applications, but also traditional
applications, such as CMS for example.
In this paper, we consider collaborative tagging in a way that addresses the prob-
lem on today’s Web of bridging the gap between wide adoptability, easy to use, and
simplicity from one side, and ability to address problems in a general way by adoption
of the formal foundation. First, the system of Web resources is modeled as a set of in-
teracting agents. We adopt a system-oriented approach to the collaborative tagging
and define it as a set of interactions in the system of Web resources. Also, we define a
concept of knowledge for individual agents based on their local state. Later we use
concepts of interaction and knowledge to give definition of a Link. In the third sec-
tion, for a given Universal Set of Resources, we introduce Tag Cloud System (TCS)
and definition of (possibly fuzzy) collections of resources. In the fourth section, we
introduce concept of Class, based on projection of collections of resources in the TCS.
In this way, we lay down some of the groundwork towards TCS-based type system.
Finally, we discuss a few pointers for future work and give some concluding remarks.
Semantics of the Concept of Resource
Debate over the concept of resource
In the early days of the Web, semantics of the resource concept has been much less
important comparing to application and adoption aspects of the concept. As a natural
consequence, the concept of resource was traditionally comprehended as a network
‘retrievable’ entity. However, mass adoption of the Web has resulted in completely
new understanding of the value of the Web. For example, Semantic Web is one of the
most promising candidate prospects. For the Semantic Web, understanding of the
concept of resource is of the paramount importance because transferring data is not
enough any more: Now, we have the need to communicate knowledge. To do so, we
have to move up the ladders of abstraction, adopt a higher meta level as an opera-
tional level, and manipulate with knowledge and interaction instead of data and com-
munication. Having that in mind, it is somewhat surprising that there is still an ongo-
ing debate over definition of the resource in the literature as well as in the community
[1,7,10,11,12].
Although there is a stated definition of a resource in the URI RFC, it is in many re-
spects vague: “A resource can be anything that has identity. Familiar examples in-
clude an electronic document, an image, a service (e.g., ‘today's weather report for
Los Angeles’), and a collection of other resources. Not all resources are network ‘re-
trievable’; e.g., human beings, corporations, and bound books in a library can also be
4 Milorad Tošić and Valentina Milićević
considered resources. The resource is the conceptual mapping to an entity or set of
entities, not necessarily the entity which corresponds to that mapping at any particular
instance in time. Thus, a resource can remain constant even when its content – the en-
tities to which it currently corresponds – changes over time, provided that the concep-
tual mapping is not changed in the process." [13].
The ongoing debate about the difficult problem of semantics of the concept of re-
source is very important, probably should receive a stronger support from at least one
official standardization organization, and involves very diversified and heterogeneous
scientific disciplines. In this paper, we do not want to get involved into the debate be-
ing aware of possible inconsistency in the rest of the paper. Instead, we give the fol-
lowing statement, based on [13,14], and consider it as correct enough for the purpose
of the paper:
Resource is a generic term for anything in the universe of discourse that has iden-
tity.
Though, having in mind very limited implementation value of the statement
[1,7,10,11,12], we allow further refinements in the rest of the paper on as needed
bases. Comparing our previous statement about resource to the definition given by
WordNet [15] that a resource is “a source of aid or support that may be drawn upon
when needed (the local library is a valuable resource)” we may say that, by our
statement, knowledge about identity of anything in the universe of discourse has a
value on its own.
Multi-agent interpretation
In our system, there are two first-class meta-classes of objects: 1) Resource and 2)
Link. All further constructs are built upon these two meta-classes of objects. As a
modeling foundation for the definitions of the resource and link concepts, we adopt an
approach that follows distributed knowledge theory developed by Joseph Halpern2,
particularly work on knowledge-based protocols [16]. In the following, we introduce
basic system modeling concepts using body of work from [16] as a foundation. How-
ever, we use the concepts introduced that way in substantially new manner such that
they provide foundation for presenting some of our original ideas, particularly ones
related to the concept of interaction.
Let us given set of entities AG={agi| i=1,2,…,n} , called agents, such that each
agent in the set carries certain amount of its own local information. The agent may
change its local information and any change of the local information is observable by
the agent. The local information is also called the agent’s local state, s(agi).
2 See http://www.cs.cornell.edu/Info/People/halpern/abstract.html for the complete list of his
work.
The Semantics of Collaborative Tagging System 5
Definition 1: The set of agents, AG={agi| i=1,2,…,n} , that may ever exist in any
system under consideration is called an universal space of resources U, also referred
to as the universe of discourse.
We consider the given set of agents AG={agi| i=1,2,…,n} , to be closed: There is
one agent in the set, called environment, that models state and interactions that are out
of scope of the modeled system. In other words, there is no agent outside the set of
agents that any agent from the set interacts with, ever: (∀agi ∈ AG ) agi • agj ⇒ agj ∈
AG .Note that the set of agents is not considered closed on its own sake. Instead, it is
closed with respect to the modeled system. In other words, the set of agents represents
our knowledge about the modeled system. Also, it represents structure that we use to
reason about the system.
If agent agi may change local state of some other agent agj, or if agent agj may ob-
serve a (certain) change in the state of the agent agi, then we say that agents agi and
agj are interacting, and such their setting is called interaction, ρij: agi • agj. Let us de-
note the set of all interactions between agents from the set AG as RAG={ρij| agi , agj.∈
AG}. We also say that an interaction protocol is initiated between two interacting
agents. Any single agent may be involved into zero, one or more interactions. Part of
the local state s(agi) that may be changed by the agent agj or that is observed by the
agent agj within the interaction ρij, is called projection of the local state on the inter-
action, and denoted as s(agi)↓ ρij. Union of projections of local state s(agi) on all ex-
isting interactions, sO(agi) = ∪ s(agi)↓ ρ for all ρ∈ RAG, is called observable part of
the local state. Local state of the interaction ρij , denoted as s(ρij), is defined as a un-
ion of projection of the local state on the interaction for each agent involved into the
interaction, s(ρij)= s(agi)↓ ρij ∪ s(agj)↓ ρij. An agent agi is called passive (active) with
respect to interaction ρij, if it cannot (may) change local state of the interaction. A
passive agent is an agent that is passive with respect to all existing interactions. An
agent that is not passive is called active agent. Observable part of local state of a pas-
sive agent can be changed only as a consequence of interaction with an active agent.
However, we say nothing about non-observable part of the local state – meaning that
an agent may change non-observable part of its state and still be considered as a pas-
sive agent.
For example, intended semantics of the interaction may be illustrated by means of
a shared variable between two concurrent threads, where threads represent agents,
ThrdA and ThrdB, and shared variable represents the interaction. If the shared vari-
able is part of local state of each of the interacting agents then each of the threads is
modeled as an active agent. However, we can consider the shared variable to belong
to the local state of only one agent. In that case, the agent having the shared variable
as part of its local state may be modeled as passive or active, while the other agent
must be active (if the other agent is not active then there would not be any interac-
tion). Note also that the interaction is about change in a state but not about data trans-
fer as is the case with a communication protocol. The important difference between
interaction and communication protocols is in the level of abstraction where the
change happens: In the case of data transfer, the change is always in the value of data.
However, in the case of interaction, the change can be in data, information, knowl-
edge, or some other interpretation. Note that different protocols represent different
6 Milorad Tošić and Valentina Milićević
kinds of possible interactions between agents. Also, different interaction protocols
may be interpreted at different meta levels. Now, let us examine the case where the
two threads communicate some data from ThrdA to ThrdB in a send-receive fashion
such that ThrdA is sending while ThrdB is receiving data. The communication proto-
col may be like this: Initially, value of the shared variable is zero; ThrdA sets new
value in the shared variable; ThrdB probes value in the shared variable permanently,
and when the value is not zero ThrdB copies the value into some other place in its lo-
cal state; After reading the value, ThrdB set value of the shared variable to zero; After
setting new value, ThrdA has started to probe the value; After registering zero value in
the shared variable, ThrdA knows that it is safe to set next value. In this case, at the
interaction level both agents are active because each of them changes value of the
shared variable. However, at the communication level, we say that the sender (ThrdA)
is active while the receiver (ThrdB) is passive.
Definition 2: Body of Knowledge (BK) of an agent agi is defined as a part of its local
state that is not observable BKi ≡ s(agi)⁄sO(agi).
Definition 3: Link is knowledge that an agent has about identity of some other agent.
The link is knowledge that is sufficient for the agent to initiate an interaction protocol
with the linked agent.
Definition 4: Let us given an interaction protocol, set of agents (called resources)
and an individual agent (called agent) such that the agent can interact with the re-
sources by the given protocol. Addressing (or Code) of the set of resources is a com-
mon service, such that there is guaranty that if an agent encounters the interaction pro-
tocol with different end addresses then it will interact with different agents, i.e. it may
eventually experience different interaction histories.
In order to give an example for the previous definitions, let us consider an agent
agnew that has just been introduced into the universe of discourse. Since agnew doesn’t
have any interaction history, it has empty body of knowledge, BKnew=∅. Because
BKnew=∅, agent agnew doesn’t know about any links and is not able to activate any in-
teraction. It has to wait for some other active agent to initiate interaction with the new
agent. After finishing an interaction, it is expected that agnew may have remembered3
something from the previous interaction such that it may now have BKnew≠∅. If agnew
have learned address of some other agent during its last interaction, then agnew may be
able to initiate interaction with the agent on that address.
3 An agent may or may not remember interaction histories depending on its internal memory re-
sources. However, taking this into consideration is definitely out of scope of the paper.
The Semantics of Collaborative Tagging System 7
Tag Cloud System
In this section, based on the previously defined concepts of resource, link, interaction,
and knowledge, we present our understanding of the collaborative tagging as a set of
concurrent acts of introducing new links into the system.
Definition 5: Tag Cloud (TC) is a tuple TC =(R,L) where R⊂U is a non empty set of
resources contained in an universal space of resources U, also referred to as the uni-
verse of discourse, L = {(r,RID(p)) | r ∈ R, p∈U} is a set of links, RID (p): R→A is a
resource identity function that is mapping from the set of resources to the set of ad-
dresses A.
Note that the previous definition introduces a purely abstract category of resource
as a member of the set of resources R and by means of the resource identity function
RID. We say nothing about what the resource is, what is it’s nature, structure, behav-
ior or else. At this point, there is no semantics assigned to the resource. Instead, the
resource can be anything that participates as a source of a link in the TC. The set of
addresses A may be subset of a language or a subset of an enumerated set. The fact
that set of addresses A is a subset of a language (or enumerated set) should be inter-
preted such that not every correct language construction is an address in the TC.
The TC represents a distributed knowledge system in a sense that we may consider
something as a resource only after we learned about it as a resource. Similarly, we
may consider a correct language construction as an address only after we learn about
it as an address of a resource in the TC. On the other hand, the only way we can learn
about new resources is to interact (inspect) with resources that are participating in our
current knowledge. Further, In other words, we cannot speculate about anything that
is not linked to at least one resource from the TC. In that way, we may say that TC
represents the Resource Universe.
The natural interpretation of the TC is set of agents, as is introduced in Definition
1. We indicate this fact by the requirement that set of resources in the tag cloud is
subset of the universe of discourse. In this way, we apply developed semantics of the
multi-agent system to the tag cloud.
Definition 6: Tag Cloud System (TCS) is a tuple TCS=(R,L,Σ), where TC =(R,L) is a
Tag Cloud, and Σ is a set of collections of resources from U such that each collection
C∈Σ is defined by the associated membership function mC.
The Tag Cloud System is a fine extension of the Tag Cloud structure that allows us
to introduce collections into the Tag Cloud. The collection is defined by means of its
membership function, with no constraints made on the function. The idea here is to
have flexibility to being able to introduce different collections with membership func-
tions of different nature, including fuzzy sets [17,18]. For example, in order to define
set of tags R as a collection in the universe of discourse U, we use the membership
function from the classical set theory: mR: U→{0,1}, where ∀u∈U, mR(u)=1 if u∈R,
and mR(u)=0 otherwise. However, we are not constrained to use such classical (or
crisp) sets only. We can also use fuzzy set, which is a more general concept then the
8 Milorad Tošić and Valentina Milićević
classical set: The membership of an element to a fuzzy set is not described by a Boo-
lean function (as it is the case for a classical set), but by real values between 0 and 1,
in general [18] (note that it can also be any other function, including discrete func-
tions).
As we mentioned before, in the interpretation of the TCS, one cannot reason over
anything else other then agents knowledge BKi. Consequently, the membership func-
tion for an agent agi must be defined over BKi only, for any collection under consid-
eration. Typically, the BKi includes addresses of other agents that are pointed to by
links starting at agent agi.. However, we do not put any restriction on the type of
knowledge that may constitute BKi. Thus, in addition to the “network topology”
knowledge, an agent may have a free form text (for example, comment of the user
who has created the agent while tagging some information on the Web), pictures, and
any other type of structured or un-structured data. Later on, this knowledge is used for
search or information extraction or any other purpose. The important advantage is that
we integrate network topology information and free form information such that it can
be queried in a unified manner.
Now, we introduce projection between two collections as a binary operation ↓ in
the set of collections of a TCS.
Definition 7: Collection C=C1↓C2 is projection of the collection C1 on the collec-
( )
tion C2, defined as C = C1 ↓ C 2 ≡ U Chdrn(o ) I U Chdrn(o ) , where ∪ is
o∈C1 o∈C 2
(fuzzy) union and ∩ is (fuzzy) intersection operations
The projection of two collections, is defined with an aim to capture semantics of
the TCS in the following way: First, find all tags of all resources from C1. Then find
all tags of all resources from C2. Finally, find the intersection of the two sets of tags.
The resulting set of tags should interpret “similarity” between collections C1 and C2.
Resource Class in the TCS
Traditionally, we define class as a collection of objects featuring some common
(set of) feature(s). Following the previously introduced definitions, we may introduce
class into the TCS in a similar way:
Definition 8: Class C in TCS=(R,L) is tuple (O,T) where O⊆R is a collection of re-
sources, called objects, and T⊆R is a collection of (meta)resources, called tags (or
features), such that every object o∈O has identical projection of the collection of it's
children into the given collection T: ∀o1,o2∈O (Chdrn(o1)↓T)=( Chdrn(o2)↓T).
More informally, the set O is interpreted as a set of objects belonging to the class
C. Set T is a subset of the set of all resources (resource universe) such that it's ele-
ments are identifiable as assigned semantics of being features. In other words, set T is
subset of tags. However, we have to have a method to identify single resource as a
The Semantics of Collaborative Tagging System 9
tag. We implement this identification such that we have defined the page Tag with as-
signed semantics that every resource R that has incoming link from the page Tag is
perceived as being a tag. The page Tag has a link to itself meaning that it is also tag.
Conclusion, Application Aspects and Future Work
To the best of our knowledge, the work presented in this paper introduces a theoreti-
cal model for semantics of the collaborative tagging systems, for the first time. We set
the foundation for further exciting developments, particularly towards overcoming the
gap between tagging as a Web 2.0 and tagging as a Semantic Web. The underlying
model of knowledge-based multi-agent system has proven to be very helpful for us in
solving practical application problems that show up during development of our tag-
ging application prototype. In the prototype4, we adopt and implement Resource and
Link concepts. In that way, we got the unified, technology transparent, Semantic-
Wiki-Tagging system. For example, according to Definition 3, each tagging contains
link to the date when this tagging has been performed. However, we do not need to
create an actual Wiki page for every such a date: agents in the system (‘Wiki pages
containing tagging data’) have knowledge about identity of the date in a form of a
Link. From the other side, the only interaction that may be initiated with the date is
‘create page’ because the date page is not able to engage into ‘view page’ interaction.
Our future research will be to address the theoretical formulation of similar issues of
the working prototype in more details.
The short indication given in the last section is particularly promising for future re-
search. Definition of a tagging framework, similar to Object Oriented Programming,
would definitely empower a whole new application space. One of the future chal-
lenges would be an object behavior within the TCS semantics. It is an open question
whether a collection of resources is a resource itself (has an address or URI) or not.
The similar problem exists with blank nodes in RDF [14,21]. Hence, we expect solu-
tions similar to the one presented in [21] to be effective in the case of TCS too.
In this paper, we introduced the theoretical foundation for addressing different as-
pects of the semantic scripting by considering the collaborative tagging as a low-level
scripting language on the global computational services fabric called “Web as a plat-
form”5. We were focused on the system aspect of the semantic scripting. Depending
on the level of abstraction6, the target application may be traditional (collaborative
bookmarks, annotations, etc.) or semantic (semantic Wiki, semantic web portal, se-
mantic e-mail, etc.) or something completely innovative and new (such as tag cluster-
ing, tag hierarchies, tag cloud management, weighting and sequencing of tags, etc.).
4 Code base of the prototype initially started as a modification of JSPWiki open source Wiki
engine [19]. However, in time they developed into two almost independent applications.
5 Analogous to shell scripting on an OS platform
6 Level at the semantic web stack [20]
10 Milorad Tošić and Valentina Milićević
We are developing several Web applications based on collaborative tagging para-
digm described in this paper. The current tagging application prototype can be ac-
cessed for testing at http://infosys-work.elfak.ni.ac.yu/InfosysWiki-v2-
1/Wiki.jsp?page=TagCloud
References
1 Dan Connolly, Untangle URIs, URLs, and URNs: “Naming and the problem of persistence,”
IBM developerWorks, http://www-128.ibm.com/developerworks/xml/library/x-urlni.html
2 Tim Berners-Lee, James Hendler and Ora Lassila, “Semantic Web”, Scientific American,
May 2001.
3 Tim O'Reilly, “What Is Web 2.0, Design Patterns and Business Models for the Next Genera-
tion of Software,” Sept. 30. 2005, Oreillynet.com,
http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html
4 McFedries, P., “Folk Wisdom”, IEEE Spectrum, Feb. 2006., pp.80-80.
5 Heather Richter, Chris Miller, Gregory D. Abowd, and Harry Funk. “Tagging Knowledge
Acquisition To Facilitate Knowledge Traceability,” in Proceedings of the Conference on
Software Engineering and Knowledge Engineering (SEKE), July 2003, pp432-439.
6 Golber, S., Huberman, B.A., “The Structure of Collaborative Tagging System”, Information
Dynamics Lab: HP Labs, Palo Alto, USA, available at: http://arxiv.org/abs/cs.DL/0508082
7 Lambiotte, R., Ausloos, M., “Collaborative tagging as a tripartite network”,
arXiv:cs.DS/0512090 v2 29 Dec 2005.
8 ACM Computing Classification System, http://www.acm.org/class/1998/
9 Champin, Pierre-Antoine; Jérôme Euzenat; Alain Mille: "Why URLs are good URIs, and why
they are not", May 2001, http://www710.univ-lyon1.fr/~champin/urls/
10 Adam Mathes, “Source vs. Resource Ontology” http://www.adammathes.com/academic/
krfo/sourceresource.html
11 Clark, Kendall Grant: “Identity Crisis,” XML.com, September 11, 2002,
http://www.xml.com/pub/a/2002/09/11/deviant.html
12 S. Pepper and S. Schwab. “Curing the Web's Identity Crisis,” Technical report, Ontopia
http://www.ontopia.net, 2003.
13 Berners-Lee, T.; R. Fielding; L. Masinter: "Uniform Resource Identifiers (URI): Generic
Syntax and Semantics", RFC 2396, August 1998, http://www.ietf.org/rfc/rfc2396.txt
14 Hayes, P., “RDF Semantics – W3C Recommendation”, http://www.w3.org/TR/rdf-mt/,
2004.
15 Wordnet, http://www.cogsci.princeton.edu/~wn/
16 Ronald Fagin, Joseph Y. Halpern, Yoram Moses, and Moshe Y. Vardi, “Knowledge-based
programs,” Distributed Computing, 10:4, 1997, pp. 199-225., also at
http://www.cs.cornell.edu/Info/People/halpern/abstract.html
17 L.A. Zadeh. Fuzzy Sets. Information and Control, 8:338--353, 1965.
18 Florea, M.C., Jousselme, A-L., Grenier, D., Bossé, É., “Combining belief functions and
fuzzy membership functions,” Multisensor, Multisource Information Fusion: Architectures,
Algorithms, and Applications 2003. Edited by Dasarathy, Belur V. Proceedings of the SPIE,
Volume 5099, pp. 113-122, 2003.
19 http://www.jspwiki.org
20 Tim Berners-Lee. Web for real people, 2005. Available at http://www.w3.org/2005/
Talks/0511-keynote-tbl/.
21 Carroll, Jeremy J.; Bizer, Christian; Hayes, Pat; Stickler, Patrick, “Named Graphs”, Journal
of Web Semantics, Vol. 3, No. 4, 2005, available at http://www.websemanticsjournal.org/
ps/pub/2005-23