A SEMIOTIC-BASED APPROACH TO THE DESIGN

OF WEB ONTOLOGIES

Júlio Cesar dos Reis

Institute of Computing (UNICAMP) and CTI Renato Archer

Rodovia Dom Pedro I, km 143, 6, 13069-901 Campinas, São Paulo, Brazil

Rodrigo Bonacin

CRP Henri Tudor and CTI Renato Archer

Rodovia Dom Pedro I, km 143, 6, 13069-901 Campinas, São Paulo, Brazil

M. Cecilia C. Baranauskas

Institute of Computing at University of Campinas (UNICAMP)

Caixa Postal 6176, 13083 970 Campinas, São Paulo, Brazil

Keywords: Ontology Engineering, Web ontology, Web Ontology Language, OWL, Semantic Web, Semantic Analysis

Method, SAM and Organisational Semiotics.

Abstract: Nowadays, Web systems generate an enormous amount of data that need to be organized, managed and

retrieved in a more efficient and accurate way. Literature has brought these concerns, trying to develop

techniques to allow the use of content by machines through Semantic Web technologies, such as Web

ontologies. However these are still insufficient to adequately deal with aspects of information modelling,

and knowledge representation. This paper studies and points out the shortcomings of these techniques, and

proposes a new approach to better design Web ontologies aided by the Semantic Analysis Method (SAM)

from Organisational Semiotics (OS). We have investigated a novel semi-automatic method that can lead to

more representative Web ontologies.

1 INTRODUCTION

Communication is a basic element for society

evolution for millennia. The writing, written press,

radio, television and more recently the Web are

some of the greatest inventions of humanity that

propitiated the information access and sharing.

These inventions have transformed the society and

boosted the development of the humanity as a

whole. In analyzing the "emergence” and

popularization of the Web, it is possible to notice

various scientific and technological advances that

have made it possible, among them: new physical

means of communication such as optic fiber

networks and wireless networks, communication

protocols, computing devices such as faster

processors and displays, rich and standard GUI

(Graphical User Interface); and more recently a great

concern in better mechanisms for managing and

retrieving data and information.

Analysing the evolution of the Web, the Web 1.0

(or first-generation of the Web) provided quick

access to large volumes of information. The

approach in the Web 1.0 was prevalent for centuries

with books and for decades with radio and

television, which we had a relationship "one-to-

many", i.e., an information producer for many

consumers. The so-called Web 2.0, besides a

"richer" GUI has also changed considerably the Web

1.0 approach, towards a relationship of "many-to-

many", to which there are some information

producers and consumers working collaboratively.

Social Network Services (SNS), Wikis, Blogs, music

and video sharing sites are examples of applications

where many people produce and consume

information in an interactive process and usually

intensively. Nowadays, literature has glimpsed the

Bonacin R., dos Reis J. and Baranauskas M.

A SEMIOTIC-BASED APPROACH TO THE DESIGN OF WEB ONTOLOGIES.

DOI: 10.5220/0003269600600067

In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations (ICISO 2010), page

ISBN: 978-989-8425-26-3

Semantic Web (SW) as an extension of the current

Web, in which well-defined meaning is associated to

information, enabling computers and people to work

better in cooperation (Berners-Lee et al., 2001).

Web systems generate a large volume of data in

various media, with complex structures highly

distributed, including the immeasurable cultural

diversity present in information produced by

people. New opportunities for advance in the Web

could be achieved through the efficient management

of this information. Nevertheless, the development

and use of the Web brings new problems that are

dependent on scientific and technological advances

in several related areas. The solution for the problem

of information modeling in the Web depends on the

understanding of information and knowledge

"nature", and on the development of complex

computational algorithms. The challenge addressed

in this paper is to understand how to structure,

model, organize, manage and promote means for

information available in Web systems be better

computationally represented, allowing more efficient

ways to access and share information.

In order to deal with this challenge, it is

necessary to combine fundamentals, theories and

methods aiming at understanding and modeling the

process of knowledge generation and sharing with

new technological approaches. Conventional

solutions and approaches of the SW are based on

“Web ontologies”. A “Web ontology” can be

understood as a specification of a conceptualization

which provides descriptions about knowledge

(Gruber, 1993). Literature has shown several

semantic problems and limitations related to the use

of Web ontology. Therefore, the goal of this paper is

to show the major deficiencies in the SW

technologies by showing its failure to resolve the

main issues; and to present a new approach to design

ontologies in the Social Web. In this approach, we

discuss how some concepts from the Semantic

Analysis Method (SAM) (Liu, 2000) could improve

the Web ontology modeling, aiming at developing

an expanded and more representative Web ontology

towards a ‘Semiotic Web ontology’.

The paper is organized as follows: Section 2

presents the theoretical and methodological

background of the paper; Section 3 presents some

current problems and limitations of the SW

ontologies; Section 4 outlines a new approach to the

design of Web ontologies using SAM, and shows a

brief illustration and discussion; and Section 5

concludes.

2 THEORETICAL AND

METHODOLOGICAL

BACKGROUND

In this section we present an overview of the SW

concepts and its technological constraints. Besides,

as a theoretical-methodological background we

present an overview of the SAM from OS.

2.1 Semantic Web and the Ontologies

The main challenge of the SW development is to

represent the meaning of the content to be machine

interpretable. The way this is done is at the heart of

the SW study. According to Uschold (2003) the

most widely accepted definition for this feature is

content usable by machines. This means having data

on the Web defined and linked in a way that they

can be used by machines, not just for displaying

purposes, although for automation, integration and

reuse across applications.

For that purpose, it is necessary to the machine to

have a model of "knowledge" about the domain, i.e.,

the available knowledge must be represented so that

the machine can "interpret" it. Tazi (1994) argues

that knowledge can be represented with the Sowa's

Conceptual Graphs. This approach is based on

Peirce's Existential Graphs, and follows the

Aristotle’s idea that each concept is represented by a

word or symbol, serving as a semantic network in

which nodes represent concepts that are related to

each other. In the SW, knowledge is represented

through computing ontologies. According to Studer

et al. (1998) ontology is a shared and common

understanding of some domain that can be

communicated between people and computers; it is a

formal specification that should be readable and

understandable by machines.

The term ontology is often used to refer to the

semantic understanding (a conceptual framework of

knowledge) shared by individuals participating in a

given knowledge domain. Semantic ontology can

exist as an informal conceptual framework of types

of concepts, and their relations named and defined in

natural language. Alternatively, it could be

constructed as a formal semantics taking into

account the domain, with the types of concepts and

their relationships defined systematically in a logical

language. Indeed within the Web environment,

ontology is not simply a conceptual framework, but

a concrete syntactic structure that tries to model the

semantics of a domain (Jacob, 2005). According to

Noy & McGuinness (2001), ontology along with a

A SEMIOTIC-BASED APPROACH TO THE DESIGN OF WEB ONTOLOGIES

number of different instances of its classes

constitutes a knowledge base. The classes are the

focus of most ontologies. Classes describe the

concepts in the domain. For instance, a class of

wines represents all wines; specific wines are

instances of this class. The Bordeaux wine is an

instance of a class of wines. A class can have

subclasses that represent concepts that are more

specific than super-classes; e.g. we can divide the

class of all wines into red, white and rosé wines.

Alternatively, we can divide the class of all wines

into sparkling wines in non-sparkling wines.

At the core of the SW technology there is a

language based on logic for knowledge

representation and inference. Computational

Languages for ontology description are designed

specifically to define ontologies. According to the

SW architecture proposed by Berners-Lee et

al. (2001), the ontology description languages are

related to other Web languages such as Resource

Description Framework (RDF), RDF Schema and

the Extensible Markup Language (XML). According

to statistics from Cardoso (2007) OWL (Web

Ontology Language) is nowadays the most common

approach for modeling ontologies in software. OWL

has three sub-languages with increasing

expressivity: OWL Lite, OWL DL and OWL

Full. OWL is currently defined by a set of

recommendations of the World Wide Web

Consortium (W3C) (W3C, 2004).

2.2 Semantic Analysis Method (SAM)

As a theoretical reference of the OS for the proposed

approach, we have used the Semantic Analysis

Method (Liu, 2000) that comes from the MEASUR

(Methods for Eliciting, Analyzing and Specifying

Users' Requirements) (Stamper, 1993). The SAM

assist users or problem owners in eliciting and

representing their requirements in a formal and

precise model. With the analyst in the role of

facilitator, the required system functions are

specified in an Ontology Chart (OC). It is worth to

mention that this concept of ontology is different

from the SW ontology. Ontology in OS represents a

business domain which can be described by the

concepts, the ontological dependencies between the

concepts, and the norms detailing the constraints at

both universal and instance level (Liu et al., 2008).

A graphic representation of a conceptual model is

called an OC. The OC describes a view of

responsible agents in the focal domain and their

pattern of behavior named affordances (Liu, 2000).

Some basic concepts of SAM adopted in this paper

are based in Liu (2000):

“The world” is socially constructed by the actions

of agents, on the basis of what is offered by the

physical world itself;

“Affordance”, a the concept introduced by Gibson

(1977) is used to express invariant repertories of

behavior of an organism made available by some

combined structure of the organism and its

environment. In SAM (Stamper, 1993) the concept

introduced by Gibson was extended by Stamper to

include invariants of behavior in the social world;

“Agent” can be defined as something that has

responsible behavior. An agent can be an individual

person, a cultural group, a language community, a

society, etc. (an employee, a department, an

organization, etc.);

“An ontological dependency” is formed when an

affordance is possible only if certain other

affordances are available. The affordance “A” is

ontological dependent on the affordance “B” means

that “A” is only possible when “B” is also possible;

“Determiners” are properties which are variants of

quality and quantity that differentiate one instance

from another;

“Specialization”, agents and affordances can be

placed in generic-specific structures according to

whether or not they possess shared or different

properties;

OS adopts a subjectivist philosophical stance and

an agent-in-action ontology. This philosophical

position states that, for all practical purposes,

nothing exists without a perceiving agent or without

the agent engaging in actions. That is to say, each

thing depends for its existence upon the existence of

its antecedents. Words and expressions we use are

names for invariant patterns in the flux of actions

and events which the agents experience. The

classical distinction between entity, attribute and

relationship disappears to be replaced by the

concepts of agents, affordances (the actions or

attributes of agents) and norms (for the socially

defined patterns of behaviour) related to their

antecedents to indicate the ontological dependency

(Stamper et al., 2000). The concepts of the Semantic

Analysis are represented by means of this agent-in-

action ontology.

We have investigated the design of Web

ontologies to deal with their problems and

limitations, as presented in the next section, inspired

on this perspective.

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

3 PROBLEMS AND

LIMITATIONS OF SEMANTIC

WEB ONTOLOGIES

Web ontologies (in OWL) have been widely used

for many purposes, such as semantic search (Bonino

et al., 2004) and content management (Mao et al.,

2006). Although literature has shown several

semantic problems and limitations related to the use

of these artifacts.

According to Carvalho (2005), even with the

advent of ontologies, there are still no tools to assist

in the organization of the information in a way

suitable for human mental operations in an

individual or societal way. In order to facilitate the

work for the computer, the organization within the

ontology is formally made, creating a fixed relation

of words. Carvalho (2005) also argues that it is

necessary to discuss the whole set of relationships

and context of information contained in

ontologies. This contextualization is generated from

a detailed study of the topics required for

understanding the subject in question. The study

asks for a number of key concepts, which summarize

the knowledge of the area. These concepts need to

be organized as a way to produce a “knowledge

tree". This tree should be able to translate that

subject, representing it as accurately as possible. By

establishing a hierarchy between concepts, it is

difficult to accurately represent different contexts,

which means that the ontology need to be attached to

a well-defined domain.

Gärdenfors (2004) argues that if we want to

consider how humans deal with concepts and their

meanings, the structures of the class relation from

SW ontologies have captured only a little part of our

knowledge about concepts. For example, we often

categorize objects according to the similarity

between them, and similarity is not a concept that

can be expressed in a natural way in a Web ontology

language. Additionally, Gärdenfors (2004) says that

a notable characteristic of human thought is our

ability to combine concepts and, in particular,

understand the new combinations of these

concepts. Furthermore, almost all Web applications

(e.g. systems of question and answering) have inputs

in the form of combinations of concepts. Therefore,

Gärdenfors (2004) states that an important criterion

for the success of the computational semantic model

is that it should be able to deal with combinations of

concepts. This author also highlights the lack of

symbolic grounding in these ontologies. The source

of the problem is that each ontology (along with its

terminology) works as a free floating island of reeds

– it has no anchor in reality. However the "meaning"

of the ontological expression does not live on these

islands. Thus, Gärdenfors (2004) proposes the

establishment of structures called Conceptual

Spaces, as a richer semantic structure underlying the

representational format. Conceptual Spaces

represent information through geometric structures

and not through symbols.

The work of Tanasescu & Streibel (2007)

describes several arguments in favor of alternative

models for knowledge representation in detriment of

traditional ontologies, such as: (1) the inadequacy of

reasoning based on categories to represent reality;

(2) the need for different representations of the same

identity according to the context; and also (3) the

difficulty for representing psychological concepts,

such as Affordances from Gibson (1977) in a

hierarchical structure. The authors argue that Web

ontologies are not yet flexible enough to match the

representational complexity of the human mind; also

they are difficult to construct. Tanasescu & Streibel

(2007) emphasize that Web ontologies are better

suited to the description of scientific fields such as

medicine and biology, which are already semi-

formal and organized into categories and

relationships.

Tanasescu & Streibel (2007) also claim that with

the advent of Web 2.0 applications there has been an

intensified use of non-structured notes, such as

tagging and Collaborative Tagging Systems (CTS).

CTS produce different results compared to using

default vocabularies for tagging, and provide users

with a simple way to make sense (meaning) to their

own content. Consequently, the authors argue that

while current investigations are still trying to

alleviate the practical problems related to the use of

ontologies, the WS can benefit from the techniques

used by Web 2.0 applications. These techniques

have spread out widely and appear to be a way to

allow users to describe their own content, since the

system cannot determine a priori the meaning of the

content. They conclude that for a faster expansion of

SW new approaches to semantic acquisition,

separated from the centralized ontologies and not

developed by experts, need to be explored. Thus,

alternatively, they introduce the proposal of Extreme

Tagging Systems (ETS), as an extension of CTS,

enabling the collaborative construction of

knowledge bases over the use of formal and

centralized ontologies for knowledge representation.

The work of Obitko et al. (2004) proposes an

alternative approach which remains using

conventional Web ontologies for knowledge

A SEMIOTIC-BASED APPROACH TO THE DESIGN OF WEB ONTOLOGIES

representation. They have described a strategy for

designing ontologies using Formal Concept Analysis

(FCA). This is a theory of data analysis that

identifies conceptual structures among data

sets. This method allows discovering the need for

new concepts and their relationships in an

ontology. FCA is based on the philosophical

understanding that a concept has two parts: (1) its

extension which consists of all objects belonging to

the concept; and (2) its intention, which includes all

attributes shared by these objects. The crucial

characteristic in this method for knowledge

representation is that it is not based on a priori

definition of classes; nevertheless the concepts are

described from their attributes. Instead to create a

class and to associate attributes to it, a concept is

built from their attributes.

These discussed studies propose both: (1) totally

alternative methods to Web ontologies for

knowledge representation in the SW; and (2) instead

of using completely alternative methods some

approaches just propose a differentiated design for

ontologies. In the next section we propose a method

to the design of Web ontologies based on SAM.

4 PROSPECTING A NEW

APPROACH TO THE DESIGN

OF WEB ONTOLOGIES

In order to produce immediate and practical results

on the SW applications, our approach employs a

different method which produces an agent-in-action

ontology, and explores how to improve the Web

ontologies using concepts from the agent-in-action

perspective. In other words, we propose to develop a

representational structure towards a ‘Semiotic Web

ontology’. It is worth to mention that it is not our

goal to refute here the SW technologies of

nowadays, neither to create a “perfect ontology”

from a theoretical point of view; but instead we

propose to expand SW techniques with methods and

techniques coming from OS.

‘Semiotic Web ontology’ is a semantic model

(computationally tractable ontology) constructed

from a semi-automatic method based on

SAM. Some theoretical and methodological

concepts of SAM are used in conjunction with other

technologies from the SW to describe

computationally tractable ontologies using OWL.

The idea is to incorporate the concepts of particular

Agents (roles) and Affordances (patterns of

behavior) arising from the SAM into an expanded

and more representative SW ontology.

It is also important to emphasize that we do not

intend to create an OC (from SAM) in OWL or to

substitute the OC at the conceptual or business level.

The use of OWL is relevant here since it is at

implementation level, thus it gives us opportunities

to improve the semantic models used in the existing

SW applications and initiatives. We understand that

this is a fast and practical way to show direct

contributions from SAM to the SW. Semantic Web

solutions like semantic search could take advantage

of the SAM. Therefore some properties from the OC

may not be fully transcribed to OWL at this time,

while other aspects such as agent-affordance

relationship are emphasized.

From a Semiotics perspective it is assumed that

the signs are socially constructed. Thereby, a

computational model that represents the semantics

from a Social Web application should contain the

agents that interpret the socially shared concepts.

With this approach we incorporate and take to SW

ontologies concerns and possible representations

arising from the Ontology in a semiotic

perspective. In addition to agents and affordances,

we have observed that SW ontologies also do not

incorporate in the model (at least explicitly) the idea

of ontological dependency relations.

In order to design the Web ontology, we first

create an OC using SAM. This intermediate

ontology diagram is important to identify the

possible agents from the context and their patterns of

behavior, and thus pass these to the (computationally

tractable) Web ontology using OWL. To accomplish

that, a set of specific heuristics is applied to derive

an initial OWL ontology. Bonacin et al. (2004)

proposed a heuristic to construct system design

UML diagrams from OC; those heuristics must be

adapted to our purpose. This approach does not

create an equivalent ontology in OWL; instead it

provides some heuristics to support the analyst

during the modeling process.

In the ‘Semiotic Web ontology’ we represent the

agents that have behaviour(s) (affordance) in a

concept (which can have determiners), and can be

important in situations of synonymous and

polysemy. For instance the concept of ‘crane’ can

mean a bird or a type of construction equipment, and

we can model it using the agents and their

affordances; e.g. a biologist, who can be model as an

agent, probably make studies about birds. To study

birds is a pattern of behaviour of a biologist (in other

words an affordance). As shown by Figure 1, ‘crane’

is a concept that can have several different

meanings, although in some context, due to the

agent and their affordances, the meaning of ‘crane’

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

is more closely linked to birds and not, for example,

to a construction equipment, that can also be

represented in the model.

Figure 1: Modeling meanings in an example of polysemy

using agents and affordances.

Figure 1 illustrates an example of modeling

using this approach in which the ‘biologist’ and the

‘civil engineer’ are agents that have affordances

connected to specific concepts. Also this model can

have relationships of specific ‘is-a’, e.g. ‘Broga’ and

‘Whooping Crane’ are specific kinds of ‘crane’. This

shows that concepts can be related to several agents

and affordances, and with other concepts,

constituting relations and representations that make

more complete ontologies compared to ontologies

described purely for a domain.

For instance, ‘crane’ can mean a construction

equipment for a ‘civil engineer’, as well as anything

else to any other agent, or have any synonym that

makes sense for an agent ‘Y’ modeled from the data

of the Web system. We can see other examples like

‘Manga’ (in Portuguese) can mean a fruit, a sleeve

as well as a color; and we can model it using the

agents and their affordances in a ‘Semiotic Web

ontology’.

In this approach, we introduce new constructions

that represent agents and affordances in OWL

ontology. The meanings of the concepts represented

in the ontology are relative to the agents. Then,

aspects such as polysemy, that is a hard problem for

SW applications, could be better treated using this

ontology.

4.1 Illustrating the Approach

The use of this approach has been utilized and

investigated in a scenario of Social Network

Services (SNS). Experiences with users of search

engines (Reis et al., 2010) point out that this kind of

association, as developed in this approach, could

contribute to more precise and adequate search

mechanisms in SNS. We illustrate a search scenario

in SNS that can be beneficiated with the ‘Semiotic

Web ontology’. From the user profile in the SNS

application, we identify the agents represented in the

ontology, and make a connection between them

(user and agents). Thus we can prioritize (or even

limit) the search space, making a relation between

the user with the ontology; e.g. if a biologist is

logged into the system (we could find that a user is a

‘biologist’ based on his/her profile) and request a

search with the keyword ‘crane’. Whether we have a

relation between the ‘biologist’ agent and the term

‘crane’ in the ontology, the results from

announcements of the SNS that could be returned

first (ranked first) should most likely be related to

the concept of crane as a ‘bird’, not to other

meaning(s) of this word (like a type of construction

equipment).

Nevertheless to a ‘civil engineer’ that makes the

search into the system about ‘crane’, probably the

results that most interest him / her are about the

construction equipment and not about ‘crane’ as a

bird. We do not mean that other results are not

required or may not be returned in response to the

engineer search, (may be the engineer could want to

know about this kind of bird). In this case the

announcements from the SNS on ‘crane’ as

construction equipment must have greater relevance

in the ranking of results. However, a user that has a

profile which fits a ‘biologist’ agent, he or she

would have the announcements about ‘crane’ as a

bird with highest priority.

The agent-affordance relation is also used to

indicate the probable meaning of the terms in an

announcement. For instance, we could verify

whether the word ‘crane’ is about ‘bird’ or

‘construction equipment’ based on the user that

posted such information. In this situation, whether

the user who submitted the announcement fits a

‘biologist’ agent, ‘crane’ would be most likely about

a ‘bird’. Otherwise whether the advertiser is a ‘civil

engineer’, in this situation ‘crane’ would also most

likely mean ‘construction equipment’. We could

have relationships between agents to verify how

much an agent is semantically close to another and

to indicate the probable meaning based on this

aspect.

4.2 Discussing the Approach

The semantic chart (from SAM) delimits the area of

operation of the context under study and identifies

the basic patterns of behavior (affordances) of the

agents. Understanding and modeling the invariants

of behaviour of human agents, including how they

A SEMIOTIC-BASED APPROACH TO THE DESIGN OF WEB ONTOLOGIES

communicate, interpret the signs and act in society is

a key point for the construction of more accurate and

flexible ontology models.

It is possible to highlight points which the OS

approach deals with the shortcomings of

conventional Web ontologies, such as the three

deficiencies presented by Tanasescu & Streibel

(2007): (1) the reasoning based just on categories to

represent reality, in OS is complemented by the

identification of agents and their affordances; also

(2) there is no different representations of the same

identity in the context, since the meaning of the

identity is relative to the agent actions, and even (3)

there is no difficulty to represent psychological

concepts since the concept of affordances (from the

cognitive psychology) is the basis for the description

of the model. Moreover, with our approach we can

build more flexible ontologies, since the concepts

are interpreted based on the patterns of behaviour of

the represented agents, no matter whether there is a

static hierarchy of concepts, because the different

contexts can be identified by the agents. Similarity

and combination of concepts could be done using

also the agent as a way to make disambiguation.

Once modeling ontologies is a hard and time

consuming task, we believe that constructing

geometric structures underlying it, as the Gärdenfors

(2004) proposes, could be not viable on a large

scale. Regarding ETS approaches, they may not be

feasible in some contexts in which non expert users

have no ability to create and manage tags.

The understanding and modeling of ontologies

using methods and techniques grounded on human

cognition and behavior are also needed to build a

Web with focus on human agents (and not just

artificial agents). Furthermore it is important to

emphasize that we want to consider the

technological work already done, looking for new

modeling methods that will complement and boost

the proposal of the SW. Several applications may

benefit of this approach, such as new possibilities for

semantic search engines in SNS that include the

agents, and create new ways to more appropriate

search for users.

In SNS contexts, Mika (2005) has already

pointed out the general advantages of incorporating

the social context into the representation of

ontologies. According to Mika (2005) creating the

link between actors and concepts into the model of

ontologies brings benefits in terms of more

meaningful and easily maintainable conceptual

structures. Mika proposed the extension of the

traditional concept of ontologies (concepts and

instances) with the social dimension, extending this

traditional bipartite model by incorporating actors.

Mika’s proposal aims at modeling networks of

folksonomies using the idea of connecting the real

user with the concept and their objects. By using our

approach with the agents’ concept and their

affordances a more general and wide-ranging of

applications is possible; moreover, it is based on a

formal method to find out the agents, affordances

and the agent-affordance relationship.

Although concepts and theories from SAM can

bring benefits to the SW models, we argue that

OWL models and OC do not replace each other.

They present distinct views and have different

proposals. While OC concerns human perception

and patterns of behaviour, and can be empirically

refuted, OWL concerns are the computer

interpretable constructs and efficient models. In our

approach, it is responsibility of the analyst to

interpret and decide how to construct better

computer interpretable models (such as OWL) from

the OC. Tools and heuristics can be used for

supporting the analyst during this process, however

only the analyst is able to connect the models and

examine their consistence with the real world.

5 CONCLUSIONS

The evolution and use of the Web over the years

have brought new challenges on modeling and

representing information. A better organization,

management and retrieval of digital content have

become a critical point to allow new opportunities

for knowledge access and sharing in the Social Web.

Therefore, there is a growing need for solutions that

deal with semantic aspects in Web Systems trying to

understand the meanings from the information and

improve their use. The Semantic Web view brings

practical techniques and solutions trying to create

content usable by machines. Nevertheless due to the

amount and complexity of data, these technologies

are still insufficient to really deal with this problem,

resulting on more sophisticated and adequate

solutions from the point of view of human agents.

As presented in this paper, literature has pointed out

some deficiencies of conventional Semantic Web

approaches. The main goal was to raise it with a

discussion for a long term work.

Hence, new approaches to better understand and

model the semantic aspects of digital content in the

Web are necessary. This paper presented an

approach based on Organisational Semiotics (OS) to

build Web ontologies. Our proposal is to design

Web ontologies aided by Semantic Analysis Method

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

(SAM). We discussed how some concepts from

SAM could improve the modeling of Web

ontologies. We showed the possible contributions to

improve it, indicating the practical and immediate

results which the approach could be empirically

demonstrated. Further work involves to develop an

expanded and more human-representative Web

ontology, as well as to present a practical example

illustrating the use of the approach. Next steps in

this research include to explore other concepts from

SAM in the modeling using OWL, as well as to

develop a semi-automatic software tool that

materializes the ideas of the approach to create the

‘Semiotic Web ontology’, including the heuristics to

aid creating an initial OWL ontology from the OS

chart.

ACKNOWLEDGEMENTS

This work is funded by Microsoft Research -

FAPESP Institute for IT Research (proc. n.

2007/54564-1) and by CNPq/CTI (680.041/2006-0).

The authors also thank colleagues from CTI, IC

(UNICAMP), NIED, InterHAD and Casa Brasil for

insightful discussion.

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A SEMIOTIC-BASED APPROACH TO THE DESIGN OF WEB ONTOLOGIES