Towards Unification of Requirements Engineering Approaches using
Semantics-based Process
Imed Eddine Saidi
1,2
, Taoufiq Dkaki
1
, Nacer Eddine Zarour
2
and Pierre-Jean Charrel
1
1
IRIT Laboratory, University Toulouse 2 Mirail Maison de la Recherche,
5 Allée Antonio Machado, 31058 Toulouse, Cedex 9, France
2
LIRE Laboratory, University Constantine 2, BP 325, Route Ain El Bey 25017 Constantine, Algeria
Keywords: Requirements Engineering, WordNet, Semantic Similarity, Concepts Abstraction.
Abstract: In Requirements Engineering, there exist different kinds of approaches such as goal-oriented, viewpoint-
oriented and scenario-oriented approaches to specify companies’ needs. These companies use these different
approaches to elicit, specify, analyse and validate their requirements in different contexts. The globalization
and the rapid development of information technologies sometimes require companies to work together in
order to achieve common objectives as quickly as possible. In this paper, we propose a unified requirements
engineering meta-model which allows cooperation in the requirements engineering process between
heterogeneous systems. This meta-model is based on the abstraction of different kinds of approaches to
benefit from all advantages that already exist in the other requirements engineering approaches while taking
into account interoperability.
1 INTRODUCTION
“Requirements engineering (RE) is the process of
discovering, documenting and managing the
requirements for a computer-based system”
(Sommerville and Sawyer, 1997a).
In Requirements engineering, companies have
different cultures and use different kinds of tools and
approaches to describe and manage upstream phases
of software projects such as goal-oriented,
viewpoint-oriented and scenario-oriented
approaches. The globalization and the rapid
development of Information Technologies
sometimes require companies to work together in
various fields including RE in order to achieve
common objectives as quickly as possible. Another
thing, these companies are not ready to agree on a
unique RE approach to cooperate because of the time
and the cost that result from the migration. Our goal
in this paper is not to impose a new way of
companies working around one approach but to
propose a unified RE meta-model (UREM) as an
intermediary of communication between different
types of meta-models of RE approaches in order to
allow cooperation between these approaches.
Bendjenna et al. (2010) have proposed an
integrated approach MAMIE which combines
different kinds of concepts: goal, scenario and
viewpoint in order to allow cooperation between
companies. In i* approach, there exists different
variations for particulars usages. Carlos and Xavier
(2011) have defined super meta-model hosting
identified variations of i* and implementing a
translation algorithm between these different
variations oriented to semantic preservation. Our
work intends to be a combination between the two
works. We propose an abstract meta-model which
allows cooperation and translation of information
between different kinds of RE approaches.
This paper is organized in six sections. In section
two we present an overview of the idea behind
UREM. In section three, we present our three-steps
unification process. In section four, we draw UREM.
In section five, we discuss the performance of the
implementation of the unification process. Finally,
we conclude and draw perspectives of this work.
2 TOWARDS UNIFIED RE
METAMODEL
The aim of this paper is to propose a unified meta-
model (UREM) that makes companies cooperate
with each other to achieve common objectives.
443
Eddine Saidi I., Dkaki T., Eddine Zarour N. and Charrel P..
Towards Unification of Requirements Engineering Approaches using Semantics-based Process.
DOI: 10.5220/0004623804430450
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge
Management and Information Sharing (KMIS-2013), pages 443-450
ISBN: 978-989-8565-75-4
Copyright
c
 2013 SCITEPRESS (Science and Technology Publications, Lda.)

UREM is an intermediary of communication and
information translation between different types of
RE Meta-models in order to allow cooperation
between them.
Each RE Meta-model is composed of a set of
concepts. Thus, communication between two RE
meta-models is a communication between different
concepts of these two meta-models. We start by a
simple example to understand the idea behind our
work. Suppose that two persons want to work
together on a common objective to achieve it as
quickly as possible. These persons speak different
Languages LangA and LangB. To make these
persons work together, we should find a common
ground that brings the two persons to understand
each other. The two persons speak languages but
these languages are different. So, to make these
persons communicate together we need a Translator
which knows the two languages (Language concept)
LangA and LangB. The concept Language is the
common ground of LangA and LangB and through
this ground there exist a translation rules to perform
two-way translation between LangA and LangB. We
say that LangA and LangB are similar in the context
that the two are languages and share Language
Concept. Thus, the concept Language is an
abstraction of LangA and LangB.
From this idea, we are looking to create a new
meta-model which is composed of a set of classes
where each class is an abstraction of a set of
concepts (similar concepts) that exist in different RE
Meta-models. From the example, we have created an
Abstract Class (language) which is an abstraction of
ClassA (LangA) and ClassB (LangB).
3 ABSTRACTION OF RE
METAMODELS STEP BY STEP
In this section, we present our approach to unify
existing RE meta-models. The principle of this
approach as mentioned in the previous section is to
find sets of new concepts that are abstractions
(merging) of different concepts from different RE
meta-models. In other words, a group of similar
concepts from different approaches represents one
abstract concept. Finding similarities between RE
concepts is then a key issue in our process. There
exist different methods to find similarities between
objects such as structural similarities (Vincent et al.,
2004) as used in a previous paper (Saidi et al., 2012),
syntactic similarities and semantic similarities.
In this paper, we adopt a more rigorous process
that is more concerned with the meaning of RE
concepts (semantic process). Our process is based on
WordNet (George, 1995) to find semantic
relationships and similarities between words which
represent RE concepts (words are the only thing that
we get to apprehend RE concepts).
WordNet is a large lexical database which is
available online and provides a large repository of
English lexical items. The smallest unit in WordNet
called synset, which represents a specific meaning of
a word. It includes the word, its explanation, and its
synonyms. Each sense of a word is in a different
synset. Synsets are equivalent to senses = structures
containing sets of terms with synonymous meanings.
Each synset has a gloss that defines the concept it
represents. For example, the words night, nighttime,
and dark constitute a single synset that has the
following gloss: the time after sunset and before
sunrise while it is dark outside. Synsets are
connected to one another through explicit semantic
relations. Some of these relations (hypernym,
hyponym for nouns, and hypernym and troponym for
verbs) constitute is-a-kind-of (holonymy) and is-a-
part-of (meronymy for nouns) hierarchies.
For example, tree is a kind of plant, tree is a
hyponym of plant, and plant is a hypernym
(abstraction) of tree. Analogously, trunk is a part of a
tree, and we have trunk as a meronym of tree, and
tree is a holonym of trunk. If there is more than one
sense, WordNet organizes them in the order of the
most frequently used to the least frequently used.
Our aim is to perform cooperation between
different types of approaches. In this paper, we
choose one approach from each type of RE
approaches in order to achieve our goal, regardless
of the RE approach chosen, our unification process is
applicable to various other approaches. In this paper,
we deal with approaches that are widely used: i*
(Yu, 1995) as goal oriented approach, CREWS
(Sutcliffe et al., 1998) as scenario oriented approach
and PREview (Sommerville and Sawyer, 1997) as
viewpoint oriented approach. We denote:
A = {A
1
= i*, A
2
= CREWS, A
3
= PREview} (1)
Each approach A
i
has a set of concepts A
i
=
{c
i1
,c
i2
,…,c
in
} and each concept c
ij
has a name and a
definition def
cij
. We distinguish two categories of
concepts:
 Concepts of category one: these concepts (names
of concepts) are represented in WordNet as
synsets and we can get directly the definition and
the different semantic relationships between
them. The most of these concepts are represented
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as one word, but it is not always the case, for
example: viewpoint concept.
 Concepts of category two: these concepts are not
represented as synsets in wordnet. These
concepts are mostly concepts which are
composed of more than one word.
We adopt an incremental process in order to create
UREM, we start with concepts of category one.
Next, we use results of category one to complete the
unification process with the concepts of the second
category and conclude UREM. Knowing that it was
possible to deal with the two categories in the same
way as described in section 3.3 for category two by
finding similarities between texts definitions. But
regarding category one, there exists a simpler way to
group similar concepts in one abstract concept. In
addition, WordNet guides us to find appropriate
names for abstract concepts (hypernyms) of category
one. We keep using these names resulting from
category one in our meta-model as extensions
(regarding meaning) of WordNet concepts (words).
The choice of an incremental process simplifies the
unification process.
The following sub-sections describe how to deal
with each category of concepts to conclude new
abstract concepts of UREM.
3.1 Tokenization
The first step of the unification process is to set up
initial data to be treated as follows:
List of tokens T which is a list of all concepts
names of the three approaches i*, CREWS and
PREview.
T
= {Actor, Task, Actor, Goal, SoftGoal,
UseCase, Scenario, Agent, Object, Action,
Event, State, Name, StructureObject,
StateTransition, Viewpoint, Concern,
Requirement, Source, History, Focus}
(2)
From the definition of each concept of category two,
we create a list of tokens (words) that are composing
concepts definitions by removing stop words (is, a,
the…). There are four concepts of category two.
T’ = {T
1
, T
2
, T
3
, T
4
} (3)
Where T
1
, T
2
, T
3
, T
4
are respectively tokens lists for
SoftGoal, UseCase, StructureObject and
StateTransition.
These lists will be used to find the appropriate
sense for each token in a list when the overall tokens
in the list are used together. The following sub-
sections describe the remaining steps of the process.
3.2 Dealing with Concepts of Category
One
The algorithm of abstraction of this category of
concepts is composed of two steps.
3.2.1 Semantic Relatedness and Word Sense
Disambiguation (WSD)
In English language, a word can have more than one
sense that can lead to ambiguity. Disambiguation is
the process of finding out the most appropriate sense
of a word (concept) that is used in a given context.
The Lesk algorithm (Lesk, 1986) uses dictionary
definitions (gloss) to disambiguate a polysemous
word in a sentence context. The idea of the algorithm
is to count the number of words that are shared
between the two glosses. The more overlapping
(overlap scoring) the words, the more related the
senses are. For example: In performing
disambiguation for the "pine cone" phrasal,
according to the Oxford Advanced Learner's
Dictionary, the word "pine" has two senses:
 Sense 1: kind of evergreen tree with needle-
shaped leaves,
 Sense 2: waste away through sorrow or illness.
The word "cone" has three senses:
 Sense 1: solid body which narrows to a point,
 Sense 2: something of this shape, whether solid
or hollow,
 Sense 3: fruit of a certain evergreen tree.
By comparing each of the two gloss senses of the
word "pine" with each of the three senses of the
word "cone", it is found that the words "evergreen
tree" occurs in one sense in each of the two words.
So, these two senses are then declared to be the most
appropriate senses when the words "pine" and "cone"
are used together.
The original Lesk algorithm only uses the gloss
of a word, and a simple overlap scoring mechanism.
An adapted version of the algorithm has been
proposed (Satanjeev and Ted, 2002) which uses
WordNet to access a dictionary with senses arranged
in a hierarchical order. This extended version uses
not only the gloss/definition of the synset, but also
considers the meaning of related words.
When trying to guess the appropriate sense of a
word in a sentence, the original Lesk algorithm does
not utilize the senses it previously assigned to the
previous words. Our implementation of the
algorithm takes into account senses that are already
assigned.
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The aim of this step is to find the appropriate
sense for each concept of category one, these
concepts are the tokens t
1
=c
1
…c
n
=t
n
of T. Thus, to
disambiguate each pair (t
i
, t
j
) of tokens in T. The
algorithm is to find the context Context(t
i
,t
j
) = (s
ik
,s
jl
)
where s
ik
and s
jl
are the most appropriate senses for
concepts c
i
(t
i
) and c
j
(t
j
) respectively. We denote:
t
i
= {s
i1
,s
i2
,…,s
io
}, t
j
={s
j1
,s
j2
,…,s
jp
} (4)
context(t
i
,t
j
) = (s
i
,s
j
) (5)
Score(s
i
,s
j
) = max({score(s
ik
,s
jl
)}
1≤k≤o, 1≤l≤p
)(6)
The algorithm is described in the following steps:
1. For each pair of tokens t
i
and t
j
in the list, we
look up and list all possible senses
{s
i1
,s
i2
,…,s
io
},{s
j1
,s
j2
,…,s
jp
}
2. For each sense s
ik
of the two tokens we list the
three following relations def(s
ik
):
a. The definition of synset which represents
synonyms def
syno
(s
ik
)
b. The definition of synset which represents the
hypernym. def
hype
(s
ik
)
c. Definitions of synsets that represent
hyponyms. def
hypo
(s
ik
)
3. Combine all possible gloss pairs that are archived
in the previous steps, and compute the
relatedness by searching for overlap. The overall
score is the sum of the scores for each relation
pair. We denote:
Def(s
ik
) = {Def
syno
(s
ik
) = x
1
, Def
hype
(s
ik
) = x
2
,
Def
hypo
(s
ik
) = x
3
}
(7)
Def(s
jl
) = { Def
syno
(s
jl
) = y
1
, Def
hype
(s
jl
) = y
2
,
Def
h
yp
o
(s
j
l
) = y
3
}
(8)
Score(s
ik
,s
jl
) = ∑
1<s<4,1<t<4
Overlap(x
s
,y
t
) (9)
Where Overlap(x
s
y
t
) is a function which counts the
number of shared words between the two definitions
x
s
and y
t
.
4. Once each combination has been scored, we pick
up the sense that has the highest score to be the
most appropriate sense for the target concept in
the selected context (T). We denote:
Score(s
i
,s
j
) = max({score(s
i
k
,s
j
l
)}
1≤k≤o
,
1≤l≤
p
) (10)
3.2.2 Least Common Hypernym and
Semantic Similarity between Two
Senses
The above method allows us to find the most
appropriate sense for each concept of category one in
T. In this step we look up the least common
hypernym (LCH) for each pair of this category of
concepts using appropriate senses. We treat the
taxonomy of hyponymy as a tree. The following
steps describe how to get the overall tree which is
composed of a set of LCH of different concepts of
category one:
1. For each sense s
i
, we build the tree Tree(s
i
) from
the node s
i
to all hypernyms of all levels. A
Tree(s
i
) is defined as follows:
Tree(s
i
) = Level(Hype(s
i
)) (11)
Hype(s
i
) = {h
1
i
, h
2
i
,…, h
n
i
} (12)
Level(x
l
i
) = Level(x
l-1
i
) + 1 | x
l
i
∈ Hype(s
i
)
(13)
Level(s
i
) = 1 (14)
Where Hype(s
i
) is the set of nodes which represent
hypernyms of different levels. Level is a function
which associates a level value for each node in the
tree.
2. Once all trees are built. We establish connections
between LCH. The least common hypernym
between two senses is the common hypernym
which have the minimum value of level between
all other common hypernyms.
3. We build a new tree Tree(T) which is composed
of the set of concepts of the first category and all
least common hypernym archived in the previous
step. Figure 1 illustrates this tree.
For example, Work is a hypernym of the two
concepts Task and Action. Task and Action are kinds
of Work and Work is an abstraction of the two
concepts. Any abstraction between two concepts
means that exist some kind of similarity between
them. P. Resnik (1999) said: “The shorter the path
from one node to another, the more similar they are”.
We use this idea to compute similarity scores in sub-
section 3.3 (The greater value of similarity score
between two concepts, the shorter path it is).
Least common hypernyms LCH
s
will be used as
concepts of the UREM. This step gives us a first
look to choose appropriate names for new abstract
concepts.
LCH
s
= {Work, Content, Event, Knowledge,
PsychologicalFeature, Quality,
AbstractEntity, PhysicalEntity, Entity}
(15)
We observe that State, Name and Source have not
common hypernyms and are not present in the tree
illustrated in Figure 1. We move these concepts to
category two to deal with them in another way. We
modify the set T’ by adding three tokens lists T
5
, T
6
and T
7
for State, Name and Source.
T’ = {T
1
, T
2
, T
3
, T
4
, T
5
, T
6
, T
7
} (16)
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Figure 1: Tree of least common hypernyms.
3.3 Dealing with Concepts of Category
Two
In the previous section 3.2 we have concluded the
set of our abstract concepts for UREM. We are
aware that where exist a common hypernym
between two concepts, there exist a path between
them. The shorter path from the first concept to the
second, the more similar they are. For example:
Task is similar to Requirement because they share
Event as hypernym but task is more similar to Work
because the distance between the two nodes is
shorter. So, the similarity score is proportional to the
path-length. There are many proposals for measuring
semantic similarity between two synsets (synsets are
concepts in our case): Wu and Palmer (1994),
Leacock and Chodorow (1998), Resnik (1995b). In
this paper, we experiment with a simple formula.
Sim(s
1
,s
2
) = 1 / distance(s
1
,s
2
) (17)
In other words, for concepts of category one, we
have found a distance between two concepts through
a common hypernym and from this distance we can
compute similarity score. So, if there exists a
similarity score between two concepts, we can create
a path between them through a common hypernym
(as an extension of this hypernym in WordNet). In
this step, we compute similarity scores between
concepts of category two by comparing text
definitions for each pair of them with LCH
s
elements. Any synset in WordNet is substitutable of
its hypernym then if we say Work for example, it
means Work, Task, Action...and so on (Task is a
Work). Each definition is composed of a set of
words.
The following steps describe the algorithm:
1. For each list of tokens T
i
(definition of a concept
in category two) in T’, we disambiguate each
token and find its most appropriate sense as
described for concepts of the first category.
2. For each pair of concepts definition (tokens lists)
T
k
and T
m
in T’, we build similarity relative
matrix of appropriate senses R[n,m] where n is
the number of tokens of T
k
and m is the number
of tokens of T
m
, and the cell R[i,j] is the
similarity score between the appropriate sense
for the token of T
k
at position i and the
appropriate sense for the token of T
m
at position
j. we compute the similarity score between
senses by finding the distance between the two
senses as described for category one concepts,
and applying the formula 17.
3. For each concept definition T
i
in T’ with each
concept of the first category. We build similarity
relative vector R[n,1] in the same way of the
previous step 2.
4. For each matrix archived in the previous steps 2
and 3 we compute the overall similarity score
between the two concepts concerned. There are
many strategies to acquire an overall combined
similarity value for sets of matching pairs, we
apply an appropriate strategy (Average
Matching) to compute the overall score. This
similarity is computed by dividing the sum of
similarity values of all match candidates (senses)
of both concepts by the total number of set
tokens. An important point is that it is based on
each of the individual similarity values, so that
the overall similarity always reflects the
influence of them.
OverallSim(x,y) = 2*Match(x,y) / |x|+|y| (18)
Once we have all similarity scores values. We can
build the total similarity matrix for concepts of
category two and abstract concepts that are resulted
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447

from the previous step (sub-section 3.2).
3.3.1 Clustering
In this step, we classify our concepts in a set of
classes, each class or in other words each cluster
groups a set of similar concepts. There exist many
strategies and methods to perform clustering from a
similarity matrix. We use hierarchical agglomerative
clustering (HAC) (Hastie et al., 2009) which seeks
to build a hierarchy of clusters. This method starts
by considering that each concept is a cluster, and
step by step, pairs of clusters are merged as one
moves up the hierarchy.
The results of hierarchical clustering are usually
presented in a dendrogram. Figure 2 illustrates the
dendrogram of concepts clustering.
Figure 2: Cluster Dendrogram of concepts.
By cutting the dendrogram at the top level we get
the followings clusters:
C
1
= {PsychologicalFeature, UseCase}
C
2
= {PhysicalEntity, StateTransition}
C
3
= {AbstractEntity, State}
C
4
= {Entity}
C
5
= {Quality}
C
6
= {Content, SoftGoal}
C
7
= {Knowledge, Source, Name}
C
8
= {Work}
C
9
= {Event, StructureObject}
In the next section we draw the unified requirements
engineering Meta-model (UREM) from these
clusters.
4 UREM CONSTRUCTION
In this section we conclude UREM Meta-model.
Clusters from C
1
to C
9
are concepts that are archived
in 3.2.2 by adding concepts of category two:
UseCase, StateTransition, State, SoftGoal, Source
and Name, StructureObject to PsychologicalFeature,
PhysicalEntity, AbstractEntity, Content, Knowledge
and Event respectively.
We keep using same names of abstract concepts
of category one for our meta-model. Each concept
will be an extension of WordNet concept. The
concept Event in UREM for example covers the
concept event as described in WordNet in addition
to the concept StructureObject as described in
CREWS approach. We denote:
Event
UREM
= Event
WordNet
∪ StructureObject
CREWS
Relationships between the abstract concepts
obtained are Hyponymy relationship or is-kind of
relationship. This relationship is modelled as
Generalization relationship in UML. We add an
abstract class Concept at the top of all these concepts
to add a specific meaning to our context that all
these concepts are concepts in Requirements
Engineering.
Figure 3 illustrates the Unified Requirements
Engineering Meta-model (UREM). A list of
concepts is written near their abstract class.
Figure 3: The Unified Meta-Model (UREM).
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448

The goal of UREM is to ensure interoperability
and information translation between three different
types of RE meta-models. For example, an Action in
a CREWS model will be translated into a Task in an
i* model via the concept Work of UREM.
5 IMPLEMENTATION
The process described in the previous sections was
implemented using C# 4.5 with Visual Studio 2012.
Regarding WordNet, Bernard Bou has developed a
MySql database that unifies many versions and
extensions of WordNet: WordNet 3.0, WordNet 2.0-
2.1, 2.1-3.0, 2.0-3.0 sensemaps, VerbNet 2.3,
XWordNet 1.1.
Unfortunately, the algorithm takes too long to
execute. For example: it takes more than 10 minutes
to disambiguate and build trees for all concepts of
category one. The implementation needs
optimization and code cleaning.
Figure 4 shows some non-functional issues in the
algorithm code.
Figure 4: Inspection of Code Issues in the algorithm.
Hierarchical clustering is performed using R
programming language and its library igraph. Igraph
includes implementation to perform clustering,
generate dendrogram, graphs and so on. Figure 2
shows a dendrogram using this library.
6 CONCLUSIONS
This paper has presented a semantic process using
WordNet to merge and group similar concepts of
different requirements engineering approaches into
abstract concepts. These abstract concepts compose
the new requirements engineering meta-model
(UREM). For a given concept, WordNet allows us to
find its abstract concepts (hypernyms).
Unfortunately, these concepts are not all
simultaneously presented in WordNet. We have
proposed an incremental process, we have started
the unification process by abstracting concepts that
are presents in WordNet (category one). This gives
us a first look about abstract concepts naming. By
computing semantic similarities in category two, we
have merged remaining concepts to abstract
concepts archived in the first step. By keeping the
same names that are used in the first step, the
resulted clusters in the second step present an
extension meaning of abstract concepts of WordNet.
UREM will be used as translator of concepts
between different RE meta-models in order to allow
cooperation between companies which use different
types of RE approaches. As a next step, we plan to
implement a translation rules between concepts
using UREM. We are looking also to implement an
interactive tool to enrich requirements visualization
and communication between different types of
approaches.
ACKNOWLEDGEMENTS
This work is supported in part by the project PHC
CMEP Tassili n° 10MDU817.
REFERENCES
Bendjenna, H., Zarour, N. E., Charrel, P. J., 2010.
Eliciting Requirements for an inter-company
cooperative information System. Journal of Systems
and Information Technology (JSIT), Vol 12 n°4,
Emerald Group Publishing, pp. 305-333.
Carlos, C., Xavier, F., 2011. A Metamodelling Approach
for i* Model Translations. 23rd International
Conference, CAiSE 2011. London, UK, June 20-24.
Proceedings pp 337-351.
Castro, J., 2011. Goal Oriented Requirements Engineering
i*, Fifth International Conference on Research
Challenges in Information Science.
George, A. M., 1995. WordNet: A Lexical Database for
English, Communications of the ACM. VOL 38,
PAGE 39-41.
Hastie, T., Tibshirani, R., Friedman, J., 2009. Hierarchical
clustering (PDF). The Elements of Statistical Learning
(2nd ed.). New York: Springer. pp. 520–528. ISBN 0-
TowardsUnificationofRequirementsEngineeringApproachesusingSemantics-basedProcess
449

387-84857-6. Retrieved 2009-10-20.
Leacock, C., Chodorow, M., 1998. Combining Local
Context and WordNet Similarity for Word Sense
Identification, chapter 11, pages 265–283. MIT Press,
Cambridge, MA.
Lesk, M., 1986. Automatic sense disambiguation using
machine readable dictionaries: how to tell a pine cone
from an ice cream cone. In SIGDOC '86: Proceedings
of the 5th annual international conference on Systems
documentation, pages 24-26, New York, NY, USA.
ACM.
Resnik, P., 1999. Semantic Similarity in a Taxonomy: An
Information-Based Measure and its Application to
Problems of Ambiguity in Natural Language, Journal
of Articial Intelligence Research 11 95-130.
Resnik, P., 1995b. Using information Content to evaluate
semantic similarity in a taxonomy. In Chris Mellish,
editor, IJCAI-95, pages 448–453, Montreal, Canada.
Saidi, I.E., Dkaki, T., Zarour, N.E., Charrel, P.J., 2012.
Towards Unifying Existing Requirements Engineering
Approaches into a Unified Model. KMIS 2012: 311-
315.
Satanjeev B.,Ted P., 2002. An Adapted Lesk Algorithm
for Word Sense Disambiguation Using WordNet,
Computational Linguistics and Intelligent Text
Processing Lecture Notes in Computer Science
Volume 2276, 2002, pp 136-14.
Sommerville, I., Sawyer, P., 1997a. Requirements
Engineering: A Good Practice Guide. John Wiley &
Sons, Inc. New York, NY, USA. ISBN:0471974447.
Sommerville, I., Sawyer, P., 1997. Viewpoints: principles,
problems and a practical approach to requirements
engineering. Computing Department, Lancaster
University, Lancaster LA1 4YR, UK, Annals of
Software Engineering 3.
Sutcliffe, A. G, Maiden N., Shailey, M., Darrel, M., 1998.
Supporting Scenario-Based Requirements
Engineering, IEEE Transactions on software
engineering, VOL. 24, NO. 12.
Vincent, D. B., Anahi, G., Maureen, H., Pierre, S., Paul,
V. D. 2004. A Measure of Similarity between Graph
Vertices: Applications to Synonym Extraction and
Web Searching. Society for Industrial and Applied
Mathematics.
Yu, E., 1995. Modelling strategic Relationships for
Process Reengineering. PhD thesis, university of
Toronto Canada.
Wu, Z., Palmer M., 1994. Verb semantics and lexical
selection. In 32nd Annual Meeting of the Association
for Computational Linguistics, pages 133–138.
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