Web of Goals: A Proposal for a New Highly Smart Web
Meriem Benhaddi
Faculty of Sciences and Techniques, Cadi Ayyad University, BP 549, Marrakesh, Morocco
Keywords: Web Future Generation, Smart Web, Smart Life, Web 4.0, Web 3.0, Web 2.0, Goal, End User, Interaction,
Context, Adaptation, Mashup, Composition, Artificial Intelligence.
Abstract: Since Web use revolution known as Web 2.0, and the birth of a third version which is the semantic Web or
Web 3.0, users needs have kept changing and becoming more demanding in all aspects of life (health,
education, economy, etc), giving rise to a new wave of principles that have emerged to constitute a new
smart web called Web 4.0, encompassing new principles, concepts and technologies that bring new
solutions. Until today there is no exact definition of Web 4.0, much less a definition of architectural
principles; however, Web 4.0 consists of the new Web generation that is built on Web 3.0 and Web 2.0
principles, in addition to new notions such as artificial intelligence, mind controlled interfaces and
intelligent goal searching engines. Web 4.0 offers more autonomy and creative opportunities to end users in
order to quickly reach their goals by efficiently express their needs, create new applications or adapt
existing ones to their personal contexts. In this paper, we give our own definition to the new smart Web 4.0
by highlighting what makes it different from the earlier Web versions; then we propose architecture
elements that will allow transforming the Web into an Ultra-Intelligent Electronic Agent. We introduce a
motivational scenario that illustrates and nurtures the feasibility of our point of view.
1 INTRODUCTION
With the recent economic and technological changes
that the world knew in the recent decades, the
principles governing the Internet are also subject to
big changes due to new usages, new needs and new
trends. The new versions of the Web - Web 2.0 and
Web 3.0 – (O’Reilly, 2007) reflect the need for end
users - with no technical knowledge and who
interact with information systems solely as final
information consumers in the context of daily life or
daily work with no intention to produce other
systems (Cypher, 1993) - to face new requirements
of speed, lightness and mobility.
Web 2.0 is a term that was first coined in 2004
by Tim O'Reilly, that means all the technologies and
uses in the World Wide Web that followed the initial
version of the web; In particular, Web 2.0 is about
new interfaces allowing end users with little
technical knowledge to interact in a simple way with
both the content and structure of pages but also with
other end users, thus creating the social Web that
encompasses collaboration and sharing, publishing
in blogs and micro-blogs, social networking,
aggregation and composition, feedback and viral
marketing, Co-development, Crowdsourcing,
Crowdfunding and SaaS (Software as a Service).
On the other side, Web 3.0 is an extension of
Web 2.0 and designates the semantic web. The
vision of the semantic web is “turning the World
Wide Web to an environment in which information
is given well-defined meaning, better enabling
computers and people to work in cooperation”
(Berners-Lee et al., 2001). The semantic theory
provides an “account of “meaning” in which the
logical connection of terms establishes
interoperability between systems” (Shadbolt et al.,
2006).
Fortunately, as the needs of web users keep
changing and becoming more demanding in all
aspects of life, the Web accompanies these needs
and invents new principles, concepts and
technologies that bring new solutions. In fact, in
recent years, new challenges emerged and consisted
for computers to help users meet their goals (needs)
that range from recording sales to a complex use
case involving various services (software, platform
or infrastructure services). Computers have to
capture users’ requirements and intelligently
transform them into adequate services. In other
words, the Web has to be parallel of and as powerful
as the human brain (Choudhury, 2014; Aghaei et al.,
Benhaddi, M.
Web of Goals: A Proposal for a New Highly Smart Web.
DOI: 10.5220/0006250306870694
In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 687-694
ISBN: 978-989-758-248-6
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
687

2012). This new shift in the Web led to what is
known today by the Web 4.0 or particularly the
WebOS (Web Operating System), which is playing
the role of a middleware that will start functioning
like an operating system to become the Ultra-
Intelligent Electronic Agent.
To the best of our knowledge, until today there is
no exact definition of Web 4.0; however there are
some characteristics that allow differentiating it from
the earlier versions. These characteristics consist
mainly in moving towards using artificial intelligent
techniques to be as a massive web of highly
intelligent interactions (Choudhury, 2014). Web 4.0
applications are mind controlled interfaces,
intelligent goal searching engines, intelligent agents,
mobile technologies, cloud computing and services,
etc (Nedeve and Dineva, 2012).
Our Web 4.0 Definition: We define the Web 4.0
as the new Web of goals as its mission is to
intelligently detect end users intentions and goals
and propose them solutions. In fact, while the
semantic web or Web 3.0 links semantically the web
resources with a descriptive layer, the new Web 4.0
will create a new layer that offers goal oriented links
and a set of intelligent operators that connect and
transform resources from the functional point of
view (see figure 1). This new smart Web will allow
us using smart objects in a smart way in order to lead
a smart life in the fields of health, education,
business, administration, leisure, etc.
Figure 1: World Wide Web evolution: from Web 1.0 to
Web 4.0.
In order to make industries aware of this new trend
and make it easy to create Web 4.0 frameworks, we
make in this paper an architecture proposal that
formalize this new version of web and offer new
ideas and principles that will help building Web 4.0
technologies. Our architecture is dedicated to
describe the functionalities that should be handled
and implemented by a Web 4.0 frameworks, and is
not a technological architecture.
The remainder of this paper is organised as
follows. Section 2 presents some motivational
scenarios which triggered the design of our
architecture. In Section 3, we review the state of the
art. Section 4 introduces the proposed architecture
and presents its major building blocks, and finally
section 5 concludes our paper and gives directions
for future work.
2 MOTIVATIONAL SCENARIO
How many of us have wished to have an intelligent
platform that understands our goals and intentions,
offers us the adequate applications that meet our
needs, allows us to easily create new applications
from small ones and automatically adapts to the
changes that may occur in our context? Such a
platform would increase end users satisfaction,
richness of the web and productivity within enterprises.
To illustrate our point of view, let us introduce
some motivational scenarios that nurture the
feasibility of our approach: Our end user, Alice,
wants to pick up her friend in the airport. In her car,
Alice uses the city navigation service that helps her
to get to the airport before the arriving time of her
friend’s plane. After arrival at the airport, Alice
wants to use the airport navigation service in order to
get different information: the gate number of her
friend’s flight, a closer parking, a currency exchange
office and a restaurant closer to the gate in order to
make a reservation 30 minutes after the arriving time
of her friend. Mark is a second user that has the
same goal as Alice but is situated in a different
airport. Mark wants to use the application created
and stored previously by Alice after adapting it to his
new context, by preserving the purpose of the
application and changing the used services by other
ones meeting the new context and proposed by the
intelligent platform.
3 RELATED WORK
Users have goals (needs) and want computer systems
to help meet them. The first primordial thing that
computer systems have to do is to capture these
users’ needs − there are several ways to capture
tasks, goals and system requirements −, then the
system has to find or to create a service that meets
this need by composing existing services. This
section discusses existing techniques for capturing
users’ needs and composing services.
3.1 Task and Goal Modeling
According to Alberto and Troutman (2003), task
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analysis is the process of breaking down a complex
task into its smaller steps or components. Paterno
(2001) states that task models are logical
descriptions of the activities, manual or mental, to be
performed in reaching user's goals. A goal is being
defined as a prescriptive statement of intent about
some system (existing or to-be) (Bernardo and
Inverardi, 2003). Task modeling has been used for
different purposes and are particularly useful when
designing and developing interactive systems
(Kritikos, Plexousakis and Paterno, 2014)
(Lankhorst, 2012).
According to Paterno (2003), task models can be
presented syntactically (textually or graphically)
(Unified Modelling Language (UML) (Miles
and Hamilton, 2006), ConcurTaskTrees), with a set
of operators for task composition, or with a level of
formality. Lankhorst (2012) list a number of task
models that have been proposed in the literature:
Cognitive Task Analysis, ConcurTaskTrees,
Hierarchical Task Analysis and KAOS.
3.2 Web User Goal Modeling
In the field of modeling web searching user goals,
Gawade and Chhajed (2014) proposed an approach
that captures end user web query goals based on
their feedback sessions; the authors allow predicting
user’s intention on web searching in order to
improve search engine relevance and user
knowledge. While Liu, Liberman and Selker (2002)
proposed an adaptive search engine interface that
uses natural language processing to parse a user’s
search goal, and uses “com-mon sense” reasoning to
translate this goal into an effective query, Rose and
Levinson (2004) proposed a framework for search
goal that allows storing web search goals as a flat list
that classifies user queries.
In order to improve computer system
development and facilitate designing and adding
new computer features, Nielsen (1994) describes a
technique for extending a task analysis based on goal
composition principle that was suggested by Clayton
(1990). This technique helps anticipating future
users’ needs and extending and combining basic
needs with general meta-goals, called goal
composition mechanisms. Nielsen (1994) discussed
three main categories of goal composition
mechanisms which are: generalization mechanisms,
integration mechanisms and user control
mechanisms. In the other hand, Bolchini and
Mylopoulos (2003) used task analysis and extended
it to propose a goal-oriented application design
approach; the authors used a goal decomposition
based on OR and AND relationship operators.
We have not found an existent work that joins at
the same time task modeling, end user goals and
service creation. The next section presents and
discusses the end user service composition known as
Mashup paradigm which emerged in the Web 2.0.
3.3 End User Service Composition with
Mashup
Mashup emerged as a new paradigm of the Web 2.0
(O’Reilly, 2007) that enables the user generation of
services and allows end users without technical
knowledge to personalize and customize their
applications (Liu, Huang, Zhao, Mei and Blake,
2014). Applications created with Mashup are called
situational applications because they allow quick
solving of problems that occur frequently in specific
situations (Ma, Lu, Liu, Wang and Blake, 2013). The
Mashup or end user service creation can be a great
help for goal creation; in fact, each goal being
concretely reached by one or more services, the end
user service creation, enhanced with semantic,
patterns and logic, allows easy and fast end user goal
achievement.
The Mashup architecture helps to fill in the gap
between the end-user and the software development;
however, no global overview is offered about the
composed services, their relationships, their
semantics and the operators that could be applied on
them. In fact, the composed services are stored as
rigid packages and thus are not considered as goals.
Our proposal offers a global perspective on end user
goals: web user goal modeling, goal storing, goal
creating with composition process, goal
transforming, etc. In the next section, we present our
way of seeing and defining goals and our new Web
4.0 architecture.
4 PROPOSED WEB 4.0
ARCHITECTURE
4.1 Goal Mashup
A goal is either a desired modification of state or an
inquiry to obtain information on the current state. By
referencing our motivational scenario in section 2,
an example of a goal, named “Airport services”, can
be decomposed into multiple sub-goals which are:
getting to the airport, making a restaurant
reservation, locating a currency exchange and paying
the parking. The creation of this goal will be realized
Web of Goals: A Proposal for a New Highly Smart Web
689

by composing and combining all the above sub-goal.
When involving end users that do not have advanced
skills in information technologies in the process of
goal creation, we should take into consideration their
mental model that characterizes their level of
perception of the used system (Mehandijev, Lecue,
Wajid, and Namoun, 2010). This mental model
differs from the mental model of programmers; in
fact, when creating new applications, end users try to
achieve a new goal by composing existing sub-goals
; each sub-goal being represented by a service. End
users are not interested in the composition process
per se, but in getting and achieving the desired
results; thus, we define our approach as goal or task
oriented instead of service oriented in order to
respect the mental model of end users.
The goal composition process leads to an
application that is composed of a set of goals;
similarly to the end user service composition-
applications called service mashups (Benhaddi,
Baïna and Abdelwahed, 2012), we call the
applications resulting from the end user goal
composition process: goal mashups.
4.2 Goal Patterns
When compositing goals/services in response to a
new need, the inexperienced end user faces many
challenges (ex. determine the types of resources, find
resources that meet the end user criteria (quality,
price, etc.), determine necessary actions for the use
of interfaces (selection problems), determine how to
arrange and coordinate resources (integration), etc).
The system has the role of helping end users to
answer these different questions, by suggesting
goals, resources, providing guidelines for the
coordination of goals and providing feedback and
documentation for each selected action. To achieve
this, we provide the end user with a set of goal
prototypes or goals patterns, that we define as
common and repetitive use cases or goal mashups,
which can also be called end users experience
patterns since they are driven from the end users
experience. They provide answers to questions like
"How to automate the execution of two consecutive
tasks - eg. Use the city navigation service and then
switch to the airport navigation service - in response
to a triggered event? - ex. presence of a person in an
airport. While software design patterns are derived
from the experience of the software developers,
goals patterns are created, improved and enriched by
end users themselves. The database of goals patterns
being built through the experience of end users, the
system will score the various components,
depending on the frequency of use, and thus offer to
the end user the best one - which has the highest
score.
4.3 Web 4.0 Architecture
In this section, we present our proposal for a new
Web 4.0 architecture. The layers of this architecture
describe the functionalities that should be handled
and implemented by a Web 4.0 framework, and do
not propose the enabling technologies of each layer.
The enabling technologies will be presented as a
future work.
The Web 4.0 architecture presented in figure 2
includes ten vertical layers that stand the process of
creating a goal-Mashup application.
4.3.1 Goal Description Language or Goal
Description Model
The aim of this first layer is the representation of
goals or tasks. Goals need to be formalized using a
description model or language that should be
expressive by offering rich operators. Presented in
section 3.1 of this paper, task models, such as UML
and ConcurTaskTrees, are to be used to describe end
user goals. A set of operators for task composition is
also a good candidate for this task; we will define a
set of operators for our composition language that
will be used in our Web 4.0 architecture (see section
4.3.4).
This layer offers a syntactic representation,
which is the lowest level of abstraction where the
goal is represented as an atomic concrete service or a
combination of concrete services. This
representation is the realization of the semantic
representation. A semantic representation can be
realized by one or several syntactic representation.
Figure 2: Web 4.0 architecture overview.
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4.3.2 Semantic Goal Description Language
or Semantic Goal Description Model
This layer offers a semantic representation of goals,
which is the highest level of abstraction where the
application is represented as a composition of
goals/sub-goals. This representation describes the
end user request; in fact, the end user is interested in
achieving an objective whatever the used services
are. When the end user context changes (see section
4.3.5), this semantic representation does not change;
in fact, the end user goal/request stays the same
while its realization can change.
A goal is also described by several semantic
information as its type, its physical environment of
execution, its objective, its frequency of execution,
its duration of execution, its degree of importance
and end users profiles that can execute this goal.
This information can be extended and enriched in
order to create semantic relationships between goals,
which is the role of the goal patterns layer (see
section 4.3.3).
4.3.3 Goal Patterns
The goal patterns layer represents the relationships
between goals based on goals descriptive
information (type, physical environment of
execution, objective, frequency of execution,
duration of execution, degree of importance and end
users profiles that can execute the goal).
Classificatory schemas are to be created and stored
in order that intelligent agents can interpret data and
make inferences (see section 4.3.6). As previously
pointed out (section 4.2), goal patterns are created,
improved and enriched by end users themselves.
4.3.4 Goal Composition Language
This layer offers a language for creating new goals
by composing existing ones. The composition
language should allow the composition of simple
and complex scenarios and use cases; rich entities,
operators and integration patterns are required to
build such advanced integration scenarios.
In our architecture, the goal composition
language model is based on the Enterprise
Integration Patterns (EIPs) (Hohpe and Woolf, 2003)
that are powerful for allowing the realization of
complex integration scenarios. Enterprise Integration
Patterns propose the best and common solutions to
integration problems. Therefore, when EIPs are
used, they enhance the quality of the integrated
applications. EIPs consist of six groups of patterns:
messaging channels, message construction, message
routing, message transformation, messaging
endpoints (message is the data unit that transfers
from one goal to another) and system management.
The syntactic and semantic formalization of the goal
composition language is presented by Benhaddi,
Baïna and Abdelwahed in (2013). Table 1 lists the
operators that are required for an efficient and rich
goal composition.
Table 1: Goal composition operators.
Group of
operators
Role of operators
Examples of
operators
Links between
goals
Allow linking goals,
ordering and
arranging them.
Simple link
(sequence)
Broadcast link
Adapter link
Goal data
construction
Specify the data
manipulated by goals:
their inputs and
outputs.
Command data
Document data
Event data
Request-reply
data
Goal data
transformation
Allow transforming
the content or the
format of data in
order to prepare it to
be manipulated by
goals.
Data enricher
Data filter
Data enveloper
Goal routing
Specify the types of
links between goals
to forward data to a
specific destination.
Data-based
router
Goal filter
Goal aggregator
Goal splitter
System
management
Dealing with
performance,
exception,
bottlenecks, etc.
Validator and
tester of goals
data
Links purger
4.3.5 Goal Context Adaptation
With the pervasive use of mobile devices and the
need for ubiquitous computing, the issue of
“context” is becoming a hot topic in human
computer interaction research and development
(Anind, 2001). For an efficient user-centric goal
creation, the “context” dimension is needed as a new
requirement in order to enhance ease of use,
efficiency and consequently end users satisfaction.
In fact, with a semi-automatic goal composition
where the end user performs a goal composition in
response to his need (request), the system should be
able to automatically adapt the composite goal if any
change occurs in the end user context (change in
place or/and in time or/and in the device, event
occurrence, etc).
Once the new application is created by end users,
it has two representations in two different levels:
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 Semantic representation: this is the highest level
of abstraction where the application is
represented as a composition of goals/sub-goals.
This representation describes the end user request
whatever the used services are. When the end
user context change, this semantic representation
stays the same while its realization can change.
 Syntactic representation: this is the lowest level
of abstraction where the application is
represented as a composition of concrete
services. This representation is the realization of
the semantic representation (one goal can have
one or several syntactic representation).
In order to automatically adapt the composite
application to any change in the end user context, the
system has to search for new services that realize the
end user goal and that correspond to the new context
of the end user. The new goal could be retrieved
from the goal patterns (see section 4.3.3) using the
operators offered by the “Logic: inference and
deduction” layer (next section).
4.3.6 Logic: Inference and Deduction
The major role of this layer is to make inferences
and deductions on stored goals. In fact, when the
user formulates a new need using the query language
(see section 4.3.8), the inference engine tries to offer
to the end user a goal that meet all the expressed
requirements. For this purpose, the end user request
is divided into two parts on which the logic engine
operates separately:
1) Functional dimension: represents the functional
needs retrieved from the end user request.
2) Contextual dimension: represents the contextual
needs retrieved from the end user request.
When analyzing the end user request, the logic
engine could face one of the following three
situations:
 A stored goal meets accurately and exactly
(functionally and contextually) the end user
request, which will be offered to the end user.
 A stored goal meets the end user functional needs
but in a different context; in this case, the goal is
transmitted to the Context Adaptation engine (see
section 4.3.5) in order to adapt the old goal to the
new end user context.
 The expressed functional needs are not offered
by any stored goal; in this case, the end user
request is divided into multiple sub-requests;
each sub-request will undergo the same process
as the initial request, and the sub-goals – results
of this operation – are transmitted to the
composition engine (see section 4.3.4) in order to
be assembled to create a new goal that meet the
end user request.
Based on the work of Rui and Butler (2003), we
define the basic logic operators – the list could be
extended:
Table 2: Goal logic operators.
Operator Role of the operator
Inclusion
One goal is containing another goal
which is a sub-goal means that the latter
is a behavior included in the sequence of
behavior performed by the former. One
goal may be included in several goals
and one goal may include several goals.
Extension
One goal is extending another goal by
introducing alternatives or exceptions at
a given extension point, respecting a
given condition.
Generalization
One goal is generalizing another goal
mean that the latter –the child – contains
all the behavior and extension points
defined in the former goal – the parent –
and inherits all its used operators.
Equivalence
Two goals are equivalent when they
serve the same purpose. Equivalence
means the same definition for two or
more goals. Equivalence could be
functional and contextual, which means
that the two goals perform the same
behavior in the same context, or only
functional which means that the two
goals have the same behavior in two
different contexts.
Precedence
One goal is preceding another goal
means that the latter is sequenced to the
behavior of the former.
4.3.7 Suggestion Engine
Another great usefulness of the goals patterns
(section 4.2 and 4.3.3) is the suggestion system. In
fact, based on their profiles described by the age, the
types of goals (work, leisure or both) they are
interested in, the areas of interest and the physical
environment, end users will be guided in the process
of goal/service composition through the database of
goals patterns that contain the possible links
(semantic functional, contextual and goal oriented
information) between the various goals.
As goals patterns are created, improved and
enriched by end users themselves, the suggestions
will also be enriched and made more efficient. Our
suggestion model is similar to e-mail interfaces - ex.
Gmail; when writing a new message, and when the
first recipient address is entered by the user, other
addresses are proposed and suggested at the basis of
the previous messages sent by this user.
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4.3.8 Protocol and Query Language
The major role of this layer is to propose a query
language that allows end users to express their needs
and to describe their future goals. This protocol and
query language should be very intuitive and user-
friendly, but also powerful and rich in order to make
rich, specific and precise description of the goals
needed by end users. The goal query language
allows querying, adding, modifying and removing
stored goals. As with SQL for structured databases
or SPARQL for the semantic web, the protocol and
goal query language allows expressing interrogative
and constructive requests using query constructs as
SELECT, FROM, WHERE, ORDER, FILTER, etc.
In addition to these features, the protocol and goal
query language is able, if the request fails, to define
the sub-goals that, combined, respond to the end user
need. The protocol and goal query language should
be able to determine an ordered set of sub-goals that
will be transmitted to the composition language in
order to compose the sub-goals and create a new
goal.
It is necessary to emphasize that the protocol and
query language is to be used by end users that have a
clear idea of their overall goal and who can
formulate it; other users who need help to set their
goals can use the suggestions provided by the
platform (see section 4.3.7).
4.3.9 Trust
To overcome the security and privacy problems, it is
very important to focus on the enterprises
governance; enterprises managers must have a clear
policy towards the use of new technologies and
create a strategy allowing the secure and successful
adoption of a Web of goals platform. The strategies
adopted should take place within the “Enterprise
Infrastructure” and the layers of the proposed Web
4.0 architecture. Barhamgi, Benslimane, Ghedira,
Tbahriti and Mrissa (2011) proposed a declarative
approach to automatically combine data taking into
consideration the data privacy constraints deduced
from privacy policies, which determine the services
that could be created by each role.
4.3.10 User Interface and Applications
A user interface, through which the end user
interacts with the system, is a very important
component. Our user interfaces owe to be smart,
intuitive and user-friendly in order to guide end
users during the process of goals creation through
the use of the semantic. In fact, in order to compose
goals, end users use their knowledge consisting on
the objective of the goal, the final result, the
frequency, the degree of importance, the duration,
etc. This end-user knowledge represents the
semantic which, alone, should be involved in the
interaction between the end user and the Web of
goals platform. Indeed, the service-to-service
interaction, which is based on the syntax, is not valid
at the interface level. The interface provides
graphical display of services (called gadget) that
represent sub-goals, which is an abstraction of
services; therefore, the interaction and
communication way at the interface level should also
be an abstraction of the communication way between
services and should rely on the semantic.
In addition to the use of semantic information,
using forms is the easiest way for end users to
interact with interfaces. In fact, Sommerville (2010)
defines five primary styles of user interaction design,
among which the forms that provide simple data
entry and that are easy to learn.
5 CONCLUSION AND FUTURE
WORK
In this paper we presented a vision for a new smart
Web that will be called Web 4.0 or Web of goal, and
which will have the mission of enhancing and
making smarter our life in different aspects. We then
proposed an architecture that will allow the new
generation of end users (Web 4.0 end users) to be
more autonomous and creative and to respond
quickly to their own needs. Our new Web 4.0
architecture provides new principles and ideas that
will accompany end users in the process of
achieving their different goals; our architecture
considers two types of end users: those who have a
clear understanding of their goal, who can express it
using a query language, and those who rely on a
suggestion engine to discover the way leading them
to their goal. Our architecture proposes also a goal
description model, a goal composition model, a goal
patterns, a logic engine, a goal context adaptation,
trust, and user interfaces and applications.
Our future work consists of exploring the
enabling technologies of our Web 4.0 architecture, in
order to build a Web 4.0 operating framework.
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