Journal of Software Engineering and Applications, 2011, 4, 356-370
doi:10.4236/jsea.2011.46041 Published Online June 2011 (
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design
Rationale Process
Sonia Gueraich, Zizette Boufaîda
Department of Computer Science, LIRE Laboratory, University Mentouri of Constantine, Constantine, Algeria.
Email: {sonia.gueraich, zboufaida}
Received May 6th, 2011; revised May 30th, 2011; accepted June 17th, 2011.
In the proposal, a construction project memory process based on the semantic annotation is presented. A project Mem-
ory concerns the representa tion and the identification of the exp erience acquired during projects realization. Th e main
feature of this approach is tha t th e seman tic an nota tion is used to build a contin uous seman tic d esign rationa le process.
We propose in this paper, a semantic traceability in four stages (identifying, structuring, annotating and integrating).
The identificatio n and the structu ring phases use a model called Extended Margu erite model which fully considers the
objectives of the project memory. The annotation phase exploits the results of precedent phase to prepare the final
phase. Examples are presented from a case study in an Algerian firm called ENMTP. The architecture supporting the
modelling engine is presented. Finally, an evaluation of the degree of the semantic annotation brought by proposed
process is given.
Keywords: Project Memory, Knowledge Capitalization, Ontology, Semantic Annotation, Design Rati on a l e
1. Introduction
Knowledge in companies becomes an organisational
asset for competitiveness and survival. One approach
for managing knowledge in an organisation, is to set up
an organisational memory management solution that
ensures the persistent storage and/or indexing of the
knowledge. An organisational memory relies on know-
ledge resources i.e., documents, people, formalised
knowledge and other artefacts in which knowledge has
been embedded.
Knowledge management is a process of clarification,
modelling, share and appropriation of knowledge. It is
in charge of capturing relevant pieces of knowledge
and providing the concerned persons with them at the
appropriate time, with the right level of details and in
an adequate format [1]. The activity of knowledge
management addresses problems of identification, ac-
quisition, storage, access, diffusion, reuse and mainte-
nance of both internal and external knowledge. The
former represents the explicit knowledge formalized
and transmitted using syntactic rules and semantic rep-
resentations, whereas the latter references to tacit
knowledge, fewtransferable and can not be considered
outside th e activi ty that i mplemen ts it.
The capitalization of know-how aims to develop the
tacit knowledge owned and implemented by designers
in order to reap the benefits. Grundstein [2] considers
four stages, which are the identification, preservation,
enhancement and updatingThe knowledge is then re-
garded as objects, modelled by methods such as
MKSM [3] and Common KADS [4]. This type of ap-
proach has enabled the development of industrial tools
that hadbrought significant gains in productivity.
In Knowledge management vision, we are interested
to a particular kind of process called Project Memory
Management. It is acquired in the context of a project
that must be saved with the knowledge to preserve its
meaning [5]. It comprises the project definition, activi-
ties, history and results [6].Numbers of knowledge
management methods con- sider a project memory as a
patrimony of knowledge of an organization. [7] Clas-
sify these methods in two main categories: knowledge
capitalization methods and direct extraction methods.
The first category uses basis techniques of knowledge
engineering. The second category, extracts knowledge
directly from the com- pany activities. Several tech-
niques are used basing on design rationale, statistical
analysis, linguistic analysis, and communication trace-
ability activities. In this paper, we propose to combine
the use of the two categories through the semantic an-
Building a Project Memory Using Semantic Design Rationale Process357
notation techniques.
The semantic annotation that we use is based on
domain ontology [8]. The Semantic Web proposes an-
notating document content using the semantic informa-
tion from domain ontologies [9]. So, semantic annota-
tion techniques can offer models to enrich these on-
tologies and to work on them to perform tasks like the
building of a company project memory.
In this work, we are interested to the design of a
project memory using a semantic annotation process.
The latter, relies on the traceability of the design ra-
tionale. One o f the principal p roblems in this tra ceabil-
ity is the dynamic semantic annotation modelling. In
other word, the problem is how to provide the project
concerned persons with the relevant pieces of knowl-
edge at the appropriate time, with the right level of
details and the adequate format of presentation.
The knowledge extracted in the initial project stages
is not complete and not coherent as the knowledge ob-
tained at the end of a project. So, we propose a seman-
tic traceability process in four stages: identifying,
structuring, annotating and integrating. In the structur-
ing phase, we use a corporate memory model inspired
from the Marguerite model [10] and we use the UML
[11] diagrams to present stereotypes of annotated ac-
In this paper, we propose an approach for building a
project memory in the context of the design rationale
and the semantic annotation. It is structured as follows.
In Section 2, we introduce the concept of a project
memory and our vision on it. Then, in Section 3, we
present the elaborated Marguerite model. The ontol-
ogies required by the annotation process are presented
in Section 4. In Section 5, we propose the annotation
process. Finally, we will compare our approach to re-
lated work.
2. Project Memory as a Company
Now days, there is an increasing economical interest in
the capitalization of dispersed knowledge (both theoreti-
cal knowledge and practical know-how) in a company.
The coherent in tegration of this disp ersed knowledg e in a
corporation is called Corporate Memory [12]. A Project
Memory (PM) is a special type concerning a project and
its realization. It includes lessons and experiences from
given projects [5]. It also, contains project definition ac-
tivities, history and results [6]. [13] Define a project
memory as knowledge and information memory acquired
and produced during the realization of projects.
We focus in this paper on the project memory in the
field of design. A project memory project must provide
access to information describing both the characteristics
of a project than those relating to the resolution of the
problems encountered during the project [5]. We inte-
grate the ontology component to provide primitives for
the description of both users and company context pro-
So, we define, the PM as project knowledge resource
including context, documents, ontology and annotations
required for describing project definition activities, his-
tory and results. For each component, we consider the
memory project features (characteristics) and the project
memory design rationale (problems). So, we obtain eight
points of views. Figure 1 presents our structure for the
Memory Project features allows describing informa-
tion and knowledge concerning project environment (or-
ganization) and its results. In the context, we define the
participants, tasks assigned, guidelines, design methods,
requirement. Documents include technical resources
(texts), models, hardware and software documentations.
A memory project features point of view supports an
organisational model which is an explicit representation
of the structure, activities, processes, flows, resources,
people, behaviour, goals, and constraints of an organisa-
tion. The corresponding ontology captures the essential
characteristics of the modelled entities and forms of rela-
tions existing between them in an unambiguous consen-
sual manner. The ontology provides the conceptual vo-
cabulary to embed semantic annotations in the PM do-
cuments. Annotations as a part of the PM can be in-
serted anywhere in a web document related to an original
document by using approporiate tags. Annotations are
also, stocked in the appropriate annotations data base.
In the project memory design rationale point of view,
the knowledge is invested in making decision and in de-
scribing the problems encountered and their resolutions.
Table 1 summarizes the PM commonents according to
the two points of view.
We have classified the problems into fo ur classes. The
first class concerns the project context problems (pro-
Figure1. Project Memory structure.
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
Copyright © 2011 SciRes. JSEA
Table 1. PM components.
features Problems resolutio
Context organization
participants projects
non reso
Document hardware
software Forms
Contents + / – / 0
Ontology concepts
relations Building
Reuse + / – / 0
Annotation instanciation
extraction Anomalies + / – / 0
the extraction parts. We consider like in [13] th at a prob-
lem can be both an objective and a part of the resolution
process. But, we differ in considering that the evaluation
of solutions can include a third aspect which is the non
resoluted case (with peer or good apreciations).
In our work, before building a PM, we propose ten
criterions for its definition. We have formulated them by
considering the oncologic, semantic and design rationale
aspects. [14] Have formulated seven requirements for
semantic annotation systems (for the document centric
model). So, we have adopted some of them to the PM
definition and formulated them as criteria. Figure 2 pre-
sents them in a Use Case UML diagram. We can resume
them in the following points:
posed design, requirements, project organization, par-
ticipants, product problems). The second class describes
documents problems (versions, authors, modifications). Criterion 1: Project Memory needs: The needs for
building a PM may depend on the company size (wide,
medium or small). We agree with [15] in the following
enumeration of these needs. 1) to avoid the loss of know
The third class permits to resence ontology problems
related specially to its construction and reuse. The final
class gives the problems encountered during the annota-
tion process like instance ambiguities and anomalies in
Figure 2. Criterion use case diagram.
Building a Project Memory Using Semantic Design Rationale Process359
how of a specialist after his mutation, death or retirement,
2) to exploit the experience acquired from past projects
in order to avoid to reproduce some mistakes, 3) to ex-
ploit the knowledge map of the company for the corpo-
rate strategy, 4) to improve information circulation and
communication, 5) to improve learning of employees, 6)
to different know how.
Criterion 2: Project Memory classifications: Co nsider s
the project characteristics and the design rationale points
of view.
Criterion 3: Project Memory model: The core in
building a PM is to be supported by a model. In our work,
we have proposed the model of the Extended Marguerite
presented in the section 3. Th is model covers the PM life
cycle through its petals.
Criterion 4: Support of heterogeneous documents: We
consider the PM as a collection of documents and the
work with multiple documents formats will become a
necessary requirement.
Criterion 5: Support of intelligent documents: [14] de-
fine an intelligent document as a document which
“knows about” its own content in order that automated
processes can “know what to do” with it. So, we can
consider an intelligent PM such a PM containing intelli-
gent documents that can be formalized as web pages.
Criterion 6: Support of documents evolution: We con-
sider that the consisten cy and the flexibility of a PM fol-
low the dynamic creation and evolution of both the
documents and the related ontologies. So, we propose to
deal with many document versions.
Criterion 7: Ontology support: To manage a PM, we
consider that the existence of one or more ontologies is
useful for a semantic point of view. The semantic anno-
tation is the bridge between the PM documents that are
in general not structured or semi structured and their se-
mantic ontological structured format. So, the changes
resulting from the ontology population during company
life cycle deal to resolve the possible inconsistency be-
tween evolving ontologies and annotation tools.
Criterion 8: User centred/collaborative design: The
conception of a PM demands the collaboration between
the different actors: experts, annotators, employers, etc.
So, the annotation tools using standard formats give
shared interfaces between enterprise actors. The sim-
plicity and the flexibility of such interfaces help them
performing the annotation task as a daily collaborative
Criterion 9: Annotation storage: The design of a PM is
a part of an organization Knowledge management Sys-
tem (KM). So, we consider the exigencies of a KM en-
vironment to the Semantic Web too ls. The Semantic Web
model assumes that annotations will be stored separately
from the original document. It decouples semantic and
content. We assume that the PM documents are separated
from their annotation storage, but with some restrictions
for particular cases for reasons of controlling document
Criterion 10: Automation: We consider that the use of
the manual annotation tools is not sufficient to follow the
company evolution. So, the use of a combination of the
automated and the manual tools seems a rational idea. So,
we propose to use some natural language processing
methods for the annotation of a PM. We motivate that in
helping company actors to exploit it efficiently without
prerequisite experiences.
In the practice we have used for each project a
criterion sheet. Then we have associated to it an EMK
(Elements of Memory Knowledge) document. It is similar
to the EC REX elements [16]. It contains a textual
description of a project task and its eventual problems
which, once restored, will be valued by users. The EMK
elements are a part of the PM model presented in the
following section.
3. Corporate Memory Model
To describe the proposed model, we presented it in the
Figure 3. The model called Extended Marguerite model
(EM) [17] is inspired from the Marguerite model elabo-
rated within the club of knowledge management [10].
The latter, was elaborated for the Knowledge Manage-
ment Life Cycle. It is composed of four processes which
correspond to the petals of the model: the process of the
capitalization and the share of knowledge, the process of
the interaction with the environment, the process of the
learning and the creativity, the process of the selection
and finally the heart which is composed by the process of
the evaluation of the knowledge patrimony.
So, we have modified this model for two reasons: the
first is to adapt it to th e conception of a PM which will be
annotated after. The second is to give a special descrip-
tion to the PM Life Cycle.
For annotating the PM and to remain in the Web Se-
mantic context, we consider the final format of the
documents as web pages. If they are not, we consider that
the PM designer must transform them to web pages
documents. We suppose also, that in this annotation
phase, we begin to construct the ontology. This initiativ e
may gain time and efforts and diminish costs.
In the EM model, we have proposed three kinds of
petals plus the heart composed from grains. The whole
take the form of a flower called the “Marguerite”. The
first category of petals is the Life cycle petals composed
of Needs Identification, Building, Use and Maintenance
petals. The second category is the Capitalization petals.
The third category is the Interaction petals. We have
summarized the essential of the model in Table 2. In the
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
Figure 3. Extended Marguerite model.
Table 2. EMM description petals.
Life cycle petals: processes of constructing the original
corporate memory
Needs Identification petal
Needed Tools
Identify users, tasks, knowledge types
they need to memorize and retrieve, ...
EMK sheets + UML diagrams
Building petal
Select the relevant documents + build
Ontology document Management +
ontology + Information Extraction
tools (IE) + UML diagrams
Use petal
Needed Tools
Exploit and broadcast PM
Semantic Web tools + Intranet/Internet
Maintenance petal
Needed tools Evaluate based on users’ satisfa c ti on .
Gain measurement techniques
Capitalization petals: ensure the recycling of the shared knowl edg e
in the enterprise
Needed tools: capitalization projec t methods
Interaction petals: ensure the interaction with the en vironment
Needed tools: Interaction methods
Heart and grains: set of the knowledge manipulated in the enterpris e
during its existence
PM components
Knowledge Management
Cooperative Work
Competence Management
Economic Intelligence
Crucial Knowledge
Project Memory Components
need identification petal, we use the EMK elements
which are built primarily on the basis of interviews with
project company experts, then on the documents analysis
and the interrogation of existing databases. An EMK is
typically composed of a header and a body (description).
The header includes a title, an origin (the name of the
project actors interviewed), the author, emission date
emission, a description of the domain and the project
context. The body contains opinions, comments and
Exploitation petal, concerns the use and the broadcast
of the constructed PM. It is supposed to be manipulated
by adequate members of the enterprise. An example of
this use is the information retrieval which is generally
applied in the info rmation search. This petal is adapted to
the common users’ activities. The broadcasting of the
PM is actually a necessary task. We have found that ac-
tually a great number of companies use the Intranet/
Internet technologies.
Capitalization petals include the process of the capi-
talization and the share of the knowledge. So, they ensure
the recycling of the shared knowledge in the enterprise.
This petal is necessary to all petals of the first category.
Interaction petals includ e the process o f the in teraction
with the environment. So, the knowledge is updated by
the information flows coming from the enterprise envi-
ronment. This petal is in relation with the exploittation
petal. Each petal has an internal link with the heart com-
ponent and especially with the PM components. Each
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process361
heart grain petals, such Cooperative Work or Economic
Intelligence reflects a part of the company orientation.
The CK and PM components enumerate and catch traces
of the different PM components. Especially they are the
providers of the onlology concepts and relations elabo-
rated in the following section.
4. PM Ontology
In the Semantic Web, the ontologies required a good life
cycle development: from the specification of needs to the
implementation. [18] Consider that some companies
build their own ontologies in order to construct a knowl-
edge-base Corporate Memory relying on them. So, in this
work we do not focus on the detailed presentation of the
methodology adopted to construct our ontology, but we
limit the purpose to some characteristics. The produced
ontology is important in order to be used after by the
semantic annotation. We are inspired from the many
methods issues from the literature in the construction of
the ontology. We have used the diagram of the Figure 4
to present construction steps and Table 3 to describe
We use domain ontology; we motivate this choice,
such in [18] by the reason to complete the definition of
the enterprise model and to describe the concepts and
their relations, related to the contents of the information
We have collected an essential part of the ontology
components from the interviews carried out with the dif-
ferent project actors and the company documents we
have, also reused some ontologies such as O’Comma [19]
ontology (which have reused many enterprise ontologies
like: Enterprise, Tove , Pme). So, in the first step we
have analyzed the informal version of the chosen onto-
logies. In the second step, we have supervised the parts
judged relevant for our context. We are also inspired
from the sinificant parts of the “Information Manage-
ment System Learner Information Package” [20]. It is
structured into eleven categories, including: Identifica-
tion, Goal (Goal), QCL (Qualifications, Certifications
and Licenses), Competence, Access, Activity, Hobby
(Interest), Affiliation, Security Key and Relationship.
The PM ontology is a multicomponent domain onto-
Figure 4. Ontology construction phases.
Table 3. Construction steps.
Construction steps Content
Needs Analysis
Based on the scenarios
Semi-structured interviews, observation
and document analysis
Identification of concepts, relationships
Diagrams and tables:
- Building concepts axonomies and a
binary relations diagram
- Building a dictionary of concepts and a
table of binary relations
- Building a table of attributes and a
logical axioms table
- Building a table of instances and a table
of instances relations
Transformation in formal format (termi-
nological level and assertional level)
DL (Description logic) based on the syn-
tax of SHIQ language
Techniques Implementation (RDFS for the T-Box and
the OWL for the A-box
Evaluation and
Approbation of the ontology by the PM
Request tools
logy composed of 5 ontologies, each concerned a precise
domain. We present in Figure 5, the principal compo-
nents of the ontology. The presentation is formalized as
UML class diagram. These components are:
Project Ontology: it is structured from the knowledge
needed during a vehicle project that reflects the
structure of a project.
Technique Ontology: it is based on the official com-
pany technical referential, corresponding to the vehi-
cle components.
Problem Ontology: it contains the problem types and
it is eventually solutions made by the pro ject experts.
Organizational Ontology: it corresponds to the com-
pany organization (actors, ta sks, management ).
Historical Ontology: it corresponds to the company
Each ontology is an n-leveled hierarchy of concepts
linked vertically by “is-a” relations and horizontally lin ks
by binary relations as “use” relations.
5. Semantic Design Rationale Process
We propose four main steps (Figure 6) for the semantic
design rationale process: identifying, structuring, anno-
tating and integrating of the PM into company environ-
ment. It is a continuous process (cycle).
The principal objective of the iden tifying step is to de-
fine the specifications of the PM system. We consider the
latter as a part of the Knowledge Management system.
This step is composed from three principal tasks: 1)
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
Copyright © 2011 SciRes. JSEA
Figure 5. Extract of elaborated ontology.
Figure 6. Semantic design rationale process.
clarification of the user’s needs to define necessary func-
tionalities of the PM system, 2) definition of the re-
quirements to be respected at the time of the systems
design and finally, 3) evaluation of the requirements de-
fined compared to the necessary functionalities.
The definition of a PM model which respects the defi-
nite requirements consist the input of the structuring
steps. It lets the clarification of the specifications, and the
definition of the PM components.
The annotation step uses the semantic web and the
automatic treatments of the natural language techniques.
It produces a PM system. Finally, the integrating step
elaborates architecture that can be compared to the given
exigencies. Since we are interested to the design activity,
we identify in each step three types of tasks. They are
similar to those defined in the design task model sug-
gested in Common KADS method. These tasks are the
clarification of the needs, the design of a produ ct solution
and the evaluation of the solution. The design actor is
generally inspired from passed experiments to realize
these tasks. He needs to consult information relating to
problems in closed experiments, to know the solutions
under some consideration, to obtain rejected options and
to give their justifications and taken decisions. He needs
to locate these problems in their context. To help him in
performing the design activity, we propose to use the
semantic annotation (manually and semi-automatic) as
the core of the design rational process. The ontology
provides the conceptual vocabulary to embed semantic
annotations in the PM system. In the case study company
intranets became the base of corporate memories. They
are often based on web technologies and can therefore
benefit from progresses made for the semantic web to
improve the web exploitability through semantic annota-
tions of its resources.
Building a Project Memory Using Semantic Design Rationale Process363
5.1. Semantic Annotation Step
Since the PM is composed of heterogeneous changing
documents, we structure them using semantic annotations
expressed with primitiv es provided by a sh ared ontology.
RDF and RDFS provide the framework to write the an-
notations. RDF enables us to describe the content of PM
documents through semantic an notations and use them to
search for information. The idea is that 1) the company
specifies concepts and their relationships in ontologies, 2)
documents of the company are annotated using these
ontologies and/ or extracting from documents themselves,
3) annotations are used to search the memory and navi-
gate into it. Figure 7 illustrates the semantic annotating
The semantic step can be performed in two ways. The
first uses the ontology. In this case, the annotation is the
instantiation of the document by ontology concepts. We
can before instantiating the ontology use UML dia-
grams elaborated from the EMK documents. We use
EMK elements (textual descriptions) because they sum-
marize the important pieces of knowledge and they are
easy to perform UML diagrams from them. These dia-
grams are used to enrich the existing ontology. The idea
is that in some times enriching ontology and then anno-
tating documents based on it can perform good annota-
tions (well accepted by the user). The second way is to
execute directly extraction techniques by referring to the
ontology. The results are annotations presented in dif-
ferent formats that can be supported by architecture and
valuated by company actors. Based on this approach, we
propose a methodology for generating semantic annota-
tions in two parallel steps. In the following section, we
present the annotation based on extraction.
5.2. Extraction Way
Based on the literature in the field of the extraction
[21,22], we propose the following phases:
The first phase is to proceed by a series of linguistic
analysis on the text in order to prepare it for the ex-
traction phase;
Figure 7. Semantic annotating step.
The second is to locate instances of ontology relations
and terms. Each relationship is characterized by a set
of verbs and verb phrases, and the occurrence of any
of these phrases in the text can be seen as an instan-
tiation of that relationship. Each sentence containing a
possible instance of terms is analyzed in order to ex-
tract the instances of ontology con cepts linked by this
The third phase is to generate a structured annotation
based on ontology. For each document, a comprehen-
sive annotation describing its content is generated as
In the morpho-syntactic analysis, we retrieve all neces-
sary knowledge characterizing the context. In our case,
this analysis includes:
Dividing the text into sentences by identifying the
boundaries of each sentence for a possible specific
Identification of basic linguistic entities (tokenization)
composed from the words (tokens). It also includes
the morphology of the entitie s (punctuation, nu mbers,
etc.) and finds the root of each entity (stemming).
Associating grammatical labelling to each token such
noun, verb, adjective.
In our architecture, we integrate two modules to per-
form the first two stages which are the Sentence-Splitter
and the Tokenizer and we use the grammatical Tree-
Tagger for labelling. The orthomatcher and the gazetteers
are also integrated. The modules are written on the base
of the GATE [23] pl at form.
In the detecting of semantic relations and terms, we
identify the semantic relationships and the concepts al-
ready modelled in the on tology. The identification of th e
different forms of syntactic relations in the document
forms the possible instances of relations formalized in
the ontology.
Semantic relationships are classified generally into
two main categories: Syntagmatic relations, which are
identifiable by a direct study of syntactic forms. Each
relationship is characterized by one or more phrases
(verbal, adjectival). These relationships are related to
their arguments at the syntactic level. Paradigmatic rela-
tions do not appear as direct syntactic relations, such as
the synonymy and the heteronomy. The detection of such
relationships usually requires human validation.
In our work, we can use the two catego ries of relations.
For the identificatio n of th e relation ships , we use a detec-
tion grammar which is a grammar that covers the differ-
ent syntactic forms of a relationship. Every relationship
is associated with one or more grammars; the application
of the grammar identifies a possible expression of this
relationship. We used JAPE language (Java Annotation
Patterns Engine) [23] to write our detection grammar of
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
Copyright © 2011 SciRes. JSEA
relationships. It can be applied on the GATE transducer.
JAPE was proposed to write grammars which, when ap-
plied, it adds information in the form of annotations. A
Jape grammar has a set of phases; each phase is a set of
rules as a pattern and action. In a rule if the pattern is
satisfied, then an action may be triggered. The right side
of a rule contains patterns that must be written in JAPE
but the left side, which contains actions, they can be
written in JAVA or JAPE.
For the generation of semantic annotations, we gener-
ate an overall semantic annotation for the whole docu-
ment. We applied an annotation generation algorithm
based on the [24] work. When an instance of a semantic
relationship is detected, the following step is to identify-
ing terms representing instances of ontology concepts
that are in the same sentence. This identification is used
to determine the terms of the ontology that can be linked
by the detected relationship. In this phase, we have used
Nomino [25] as a term extractor tool to provide the can-
didate terms. Then we have written a wrapper that filter
the list of candidate terms and kept only the instances of
ontology concepts.
5.3. Instantiation Way
In this step, we use manual annotations by exploiting
existing tools such ontomat [26]. For enriching the on-
tology by UML diagrams, we are inspired from the work
of [27].
We have concretized the validity of the approach
through an architecture (Figure 8) used in an Algerian
industrial company.
5.4. Case Study
The case study used is the project of prototyping cranes
and loaders vehicle in ENMTP Company. The latter, is
specialized in the development of all manufacture and
distribution process related to public work s equipment of
mechanical industry.
We have observed many problems that increase the
costs and times in design projects. For example last years,
Figure 8. Approach architecture.
Building a Project Memory Using Semantic Design Rationale Process365
the company has known a set of voluntaries departures
that have caused the loss of important mass of experience
and knowledge. So, one solution can be the store and the
management of the relevant prototyping knowledge. This
makes the knowledge patrimony available for future
The vehicle product development cycle is made of re-
petitive sub-cycles (design/development/validatio n/main-
tenance). During a vehicle project design, the design de-
partment checks that the parts satisfy the requirements of
the product specifications. The project is starting from
the most elementary functions until the final product de-
We have investigated through interviews with 35 pro-
ject actors, the importance of the 10 criteria. We have
summarized the results in Tab le 4 and presented them by
Figure 9. For each criterion, we have numbered the in-
terested actors, and then we have established the associ-
ated percentages.
We have observed that they are interested particularly
by criterion 1, criterion 4, criterion 6, criterion 8 and cri-
terion 10. So, the users are interested by knowing if there
is a real need to implement a project memory, they are
influenced by the evolution and the nature of the ma-
nipulated documents. The users also, want to obtain
adequate software and hardware that facilitate their d aily
tasks. The remaining criteria, for the moment are not
very attractive for them and for this reason; we have in-
vestigated in the semantic web field by incorporating
annotation and ontology tools.
In this section we showcase the efficiency of the pro-
posed approach, through using a scenario, as mentioned
5.4.1. Description of the Sce nar i o
The scenario begins in the company with the choice of a
project. In our case, we have chosen a project called “ex-
tension of cranes and loaders”. It is a project initialled
two years ago. In this project, the designers need to in-
clude and understand how concerned materials problems
can be resolved. They have observed that these problems
can to be similar to those in other projects, having the
same objective but in different contexts.
The modelling of the PM documents begins by defin-
ing the crucial documents containing explicit or implicit
(case of experiences) knowledge. So, we propose a struc-
Table 4. Criteria initially evaluated.
criteria percents Criteria percents
Criterion 1 25% Criterion 6 10%
Criterion 2 5% Criterion 7 5%
Criterion 3 5% Criterion 8 15%
Criterion 4 15% Criterion 9 5%
Criterion 5 5% Criterion 10 10%
Figure 9. Criteria graphically presented.
ture for each type of PM documents inspired from Rex
elements, called EMKs. These structures were modelled
by UML (Unified Modelling Language) class diagrams,
and then they are coded in XML (eXtensible Markup
Language) to be exploited as Semantic Web (SW) re-
We propose initially to generate empty XML docu-
ments to fill by designers (manually annotation). Then,
XSL sheets (extensible Stylesheet Language) were used.
They control page setting of XML generated documents
and let to visualize them by a web navigator. The docu-
ments can thus be stored in XML data bases. In our case,
we have generated 30 XML files (12 files for cranes and
18 for loaders).
5.4.2. Cons tru c ti on of the Ontology
From a syntactic point of view, XML is considered as the
standard layer that can benefit from all of the technolo-
gies developed around the web [9]. From a semantic
point of view, the goal of the semantic annotation pre-
sented by XML is to enrich both the syntactic structures
of documents and their semantic contents based on on-
The implementation of ontology, built as part of this
work is done in OWL (Ontology Web Language) in
PROTEGE 3.1. OWL is the standard recommended by
the W3C. Its advantage, in addition to its expressive
power resides in the power of the inference services that
are offered by RACER reasoner.
Racer can interpret OWL documents and offers rea-
soning services for both ontology levels: terminology
(T_Box) and assertion (A_box) levels. At the termina-
logy level, different types of queries can be used to: ver-
ify the consistency of a concept, to check subsumption
relations. At the assertion level, we can check th e consis-
tency of the extracted instances of a particular class
(concept), even according to various criteria, based on
roles and attributes of the instances. We have used, the
RQL (Racer Query Language) language, for querying
ontology et the assertion level.
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
The constructed ontology is composed from 219 con-
cepts. These concepts are characterized by 20 attributes
and 14 different relationships (7 inverse relationships
included). To populate the ontology, we have given
about 30 instances (on concepts and roles). Figure 10
presents the implemented ontology interface in PRO-
5.4.3. Sem ant ic Annotation Approach
To illustrate our approach, we propose the following
example: a designer after formulating an EMK document,
he found the following sentence which described the
problem of the not complete design in a crane engine.
The EXTC1 problem concerns project s of crane pro-
The designer based on the ontology, navigates the im-
plemented version and tries to check in the sentence the
existence of ontology instances. We have proposed the
use of the ontomat tool to help the designer in his search.
The results of this task are seen as a semi-automatic an-
notation. Then, the designer can summarize the result as
presented in Figure 11.
In the proposed approach, after, the morpho-syntactic
analysis step, the sentence is divided in labelled words
(EXTC1 is subject, concerns is verb). Then, after onto-
logical step, we know that EXTC1 is an instance of the
PROBLEM concept related to 2010 which is an instance
of PROJECT concept. The generator module generates
RDF documents and stores them in annotation databases.
To evaluate the annotation in this step, we present
some results of both quantitative and qualitative aspects
for generating annotations. We believe that an ev aluation
phase is necessary because the generation phase is costly
and generally irreversible. The quality of a semantic an-
notation (manually or generated semi-automatically),
depends mainly on the quality of the method used for
extracting information. In our work , evaluating quality of
annotations consists to:
Test the ability of detection grammars to detect all
instances of possible semant i c relationships.
Test the capacity of the terms extractor to identify all
instances of concepts that can be linked by these rela-
Link good terms with the good relationship, while en-
suring consistency with the domain model (i.e. on-
Figure 10. Ontology interface.
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process367
Figure 11. Designer annotation result.
To do this, we have exploited two measures frequently
used in extraction methods, namely precision and recall:
The precision (P) is the percentage of correctly extracted
terms. It measures the absence of noise in the extraction.
The recall (R) is the percentage of terms extracted com-
pared to that should have been extracted. It measures the
absence of silence in extraction. Another metric can be
calculated by combining the two measures, it is the
F-measure = 2PR/(P+R)
So, for the validation of the annotation modules (term
extractor), we randomly selected 10 sheets (65 words),
which we performed for the ex traction. Results were th en
compared with the manual annotations to calculate preci-
sion, recall and F-measure (Table 5).
The Precision line shows that the terms correctly ex-
tracted cover 56% of the Crane sheet and 53% of the
Loader sheet. The majority of the failures concerns typ-
ing errors due to the ambiguity. We also noticed that
some terms were considered correct but useless for pro-
ject designers because they describe either basic know-
ledge or broad knowledge. We have therefore introduced
a new quality measure called utility to measure the rate
of useful project results.
Utility (Results) = (number of results judged correctly)/
(number of results evaluated correctly)
Table 5. Example of Crane and Loader sheets evaluations.
Crane sheet Loader sheet
Manual-Annot 25 15
Extracted 18 14
Correctly-extracted 14 8
Recall 0.77 0.80
Precision 0.56 0.53
F-measure 0.64 0.63
We note that the value of this measure can be subjec-
tive as it is related to the u ser (group) point of view. The
annotations judged useful for future projects are kept in
the annotation database. We can enrich this database by
adding metadata on these annotations that help in filter-
ing users’ queries. The validation of Results can also be
integrated into the overall annotation process through
dedicated interfaces.
We have again interviewed the 35 project actors to see
the changes in their vision after the application of our
approach. We have observed that they became particu-
larly interested by criterion 7 and criterion 9 associated to
the ontology and annotation aspects. The results are pre-
sented by Table 6 and Figure 12.
6. Related Work
We present some related works, according to two aspects:
Project Memory and Semantic Annotation.
For the project memory, since we are interested to de-
sign applications, we have studied numbers of them.
Various works was interested to knowledge capitaliza-
tion. Many methods are proposed such IBIS, EMMA,
QOC, DRCS, DRAMA, SAGACE [1]. We can notice
important differences between our approach and similar
approaches. In the project decision making, some of
them use trees based on (Question/Position/Argument) or
based on (Question/Option/Criterion), graphs (Entity/
Relation), and models. In our approach, we use EM
model which is integrated in a semantic traceability
Table 6. Criteria recently evaluated.
Criteria percents Criteria percents
Criterion 1 25% Criterion 6 10%
Criterion 2 5% Criterion 7 10%
Criterion 3 5% Criterion 8 10%
Criterion 4 10% Criterion 9 10%
Criterion 5 5% Criterion 10 10%
Figure 12. Criteria graphically presented.
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
Other, works are interested to the integration of the
Product/Process/Resource as three fundamental concepts
in the description of an enterprise system. Examples of
models were presented such as: FBS model (Function,
Behaviour and Structure) [28] and FBS-PPRE (Function ,
Behaviour, Structure, Process, Product, Resource, Exter-
nal) [29]. In our case, we have integrated the different
pieces of the knowledge in ontology model. In his work,
Smain Bekhti [30] relied on the work of [31]. He pro-
poses a memory model using the formalism of semantics
networks. His project memory is divided into a rational
design memory (includes problem-solving and decision
making components) and into a context memory repre-
senting characteristics of the project. The context mem-
ory is the set of elements that characterize the conduct
and the organization of a project design. Smain Bekhti
models do not take the advantage of more recent devel-
opments object-oriented modelling including the gener-
alization/specialization aspects In our work, we share the
idea that a rationale design memory is invested in defin-
ing objectives, solving problems and making decisions
during a project. Also, we need to use a process of capi-
talization, because it allows obtaining the different traces
of the project memory. In our process we take into ac-
count the aspects of development, documentation, or-
ganization and justification. [31] Define a memory using
Entity/Association formalism. It includes a product
model to describe the various facets of product design
and a process design model. The latter, describes the
product design at different levels of detail showing why,
how, who or what is on each stage of design. The force
of this model resides in its strong integration between
sub-models. However, it does not take into account the
documentation, the justification and the argument of
choices-making aspects and do not include object-ori-
ented techniques. In [32] work, the main objective was
the specification of a knowledge management tool for the
design projects. The proposed model used domain archi-
tecture which, through instantiation and fitness (for a
particular context) can realize a project memory. He used
UML graphs. We agree, with him for this choice, be-
cause UML diagrams are well suited for inexperienced
users, they offer relative clarity and more ability to rep-
resent fairly and accurately the real world elements.
Adding, to that, it is possible to move efficient models to
computer implementation.
For semantic annotation, KIM [33] identifies the Key
Phrases and Named Entities (NE) and perfo rms semantic
annotation automatically in respect to their ontology.
They use GATE platform. Samovar [34] is a system de-
veloped to capitalize knowledge in the automotive field
(Renault). Samovar’s approach is based on the use of
multiple ontologies (Problem, Component, Service and
Project) built from databases and textual data extracted
automatically from the databases. Ontologies are repre-
sented in RDFS and used to create annotations on RDF.
CA Manager [35] is a generic framework supporting
semantic annotation based on any information extraction
tool, and also any domain ontology. The force of this
work is in preserving independences between the content
augmentation tool and the semantic repositories. We
have considered this point in our annotation process. Our
approach is more exible in the sense that we do not re-
strict, the semantic annotation to only on the ontology,
we can consider the tex tual elements.
7. Conclusions
We presented semantic design rationale process. It
elaborates the EM model which serves to the design of a
Project Memory dedicated to an industrial company. This
process is applied especially for the product design ap-
plications. The model relies on the more general knowl-
edge management phases. It uses UML diagrams to pre-
sent the context, the documents, the ontology and the
semantic annotation. The latter, is based on domain on-
tology. The semantic annotation process is controlled by
allowing human annotators to be involved. The main
contribution of the ME model in comparison to other
similar models is that it allows easy plug-in of know-
ledge management approach (that focuses on a project
memory), semantic design rationale process and onto-
We have created ontology for the ENMTP Company.
We have also, used a semi-semantic annotation process
on a project memory corpus by using instantiation way.
The annotations based on extraction reach the level of
human annotators. So, we could calculate precision and
recall values. The objective is to evaluate the exibility
and the scalability of the approach and therefore realize
the suitability for practical applications.
[1] R. Dieng, O. Corby, F. Gandon, N. Matta and M. Ribière,
“Kowledge Management: Méthodes et Outils pour la
Gestion des Connaissances,” 3th Edition, Dunod, 2006.
[2] M. Grunstein and J.-P. Barthès, “An Industrial View of
the Process of Capitalizing Knowledge,” Proceedings of
ISMICK’96, Rotterdam, Vol. 1, October 2006, pp. 258-
[3] J. L. Ermine, M. Chaillot, P. Bigeon, B. Charreton and D.
Malavielle, “MKSM: Méthode Pour la Gestion des
Connaissances,” Ingénierie des systèmes dInformation,
AFCET-Hermès, Vol. 4, No. 4, 1996, pp. 540-575.
[4] G. Schreiber, H. Akkermans, A. Anjewierden, R. Dehoog,
N. Shadbolt, W. Vandervelde and B. Wielinga “Know-
ledge Engineering and Management: The CommonKads
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process369
Methodology,” The MIT Press, ISBN 0-262-19300-0,
[5] J. Pomian, “Mémoire d’Entreprise Techniques et Outils
de la Gestion du Savoir,” In: Sapientia, Ed., 1996.
[6] P. Tourtier, “Analyse Préliminaire des Métiers et de Leurs
Interactions,” Rapport intermédiaire du projet GENIE,
INRIA-Dassault-Aviation, 1995.
[7] S. Bekhti, “DYPKM: Un Processus Dynamique de
Définition et de Réutilisation de Mémoire de Projet,”
Doctorate Thesis at UTT, In Networks, Knowledge and
organizations, December, 2003.
[8] T. R. Gruber, “Toward Principles for the Design of On-
tologies Used for Knowledge Sharing,”, In: N. Guarino
and R. Poli, Eds., Formal Ontology in Conceptual Analy-
sis and Knowledge Representation, Kluwer Academic,
[9] T. Berners-Lee, J. Hendler and O. Lassila, “The Semantic
Web,” In Scientific American, May 2001, pp. 35-43.
[10] J. L., Ermine “Capter et Créer le Capital Savoir,” Annales
des Mines, November 1998, pp. 82-86.
[11] OMG, “Unified Modeling Language: Superstructure,”
2nd Edition, 2004.
[12] L. Steels, “Corporate Knowledge Management,” In Barthès
Proceedings (ISMICK'93), Compiègne, 1993, pp. 9-30.
[13] N. Matta, O. Corby and M. Ribiere, “Méthodes de
Capitali-Sation de Mémoire de Projet,” Projet ACACIA,
Rapport de recherche, No. 3819, 1999.
[14] V. Uren, P. Cimiano, J. Iria, S. Handschuh, M. Var-
gas-Vera, E. Motta and F. Ciravegna, “ Semantic Annota-
tion for Knowledge Management: Requirements and a
Survey of the State of the Art,” Web Semantics: Science,
Services and Agents on the World Wide Web, Vol. 4, No.
1, 2006, pp. 14-28.doi:10.1016/j.websem.2005.10.002
[15] R. Dieng, O. Corby, A. Giboin and M. Ribière, “Methods
and Tools for Corporate Knowledge Management,” In-
ternational Journal of Human-Computer Studies, Vol. 5,
1999, pp. 567-598.
[16] CEA, “Les Connaissances, un Capital à Gérer,” Les dés
du CEA, No. 58, 1997, pp. 11-20.
[17] S. Gueraich and Z. Boufaida, “Building a Corporate
Memory Through Collaborative Semantic Annotation and
Ontology Population,” International Conference on Web
and Information Technologies (ICWIT’2010), Maroc,
June 2010, pp. 523-529.
[18] A. Abecker, A. Bernardi, K. Hinkelmann, O. Kuhn and M.
Sintek, “Toward a Technology for Organizational Memo-
ries,” IEEE Intelligent Systems, May-June 1998.
http:// citeseer
[19] F. Gandon, “Distributed Artificial Intelligence and
Knowledge Management: Ontologies and Multi-Agent
Systems for a Corporate Semantic Web,” Scientific Phi-
losopher Doctorate Thesis in Informatics, INRIA and
University of Nice-Sophia Antipolis, Nice, 2002.
[20] D. Bahloul, “Une Approche Hybride de Gestion des sur
les Ontologies Connaissances Basée: Application Aux
Incidents Informatiques,” Thesis In Informatics, Ecole
doctorale: Informatique et Information pour la société
(EDIIS), Lyon, 2006.
[21] K. Khelif and R. Dieng-Kuntz, “Ontology-Based Seman-
tic Annotations for Biochip Domain,” Proceeding of
EKAW 2004 Workshop on the Application of Language
and Semantic Technologies to support KM Processes,
U.K., 2004.
[22] K. Khelif, R. Dieng-Kunt z and P. Barbry , “Semant ic Web
Technologies for Interpreting DNA Microarray Analyses:
The MEAT System,” Proceedings of WISE’05, New
York, 2006.
[23] H. Cunningham, D. Maynard, K. Bontcheva and V. Tab-
lan, “GATE: A Framework and Graphical Development
Environment for Robust NLP Tools and Applications,”
ACL’02.F, 2002.
[24] K. Khelif, “Web Sémantique et Mémoire d’Expériences
Pour L’Analyse du Transcriptome,” Doctorate Thesis at
University of Nice-Sophia Antipolis, Nice, 2006.
[25] S. David and P. Plante, “De la nécessité d'une approche
morphosyntaxique dans l'analyse de textes,” Intelligence
artificielle et sciences cognitives au Québec, Vol. 3 , No.
3, 2006, pp. 140-154.
[26] S. Handschuh, S. Staab and R. Studer, “Leveraging
metadata creation for the Semantic Web with CREAM,”
Proceedings of the Annual German Conference on AI,
Germany, September 2003.
[27] M. Mhiri, A. Mtibaa and F. Gargouri, “Towards an Ap-
proach for Building Information Systems’ Ontologies,”
1st workshop Formal Ontologies Meet Industry, Verona
June 2005.
[28] X. Hu, “A Survey on Design Rationale: Representation,
Capture and Retrieval,” Proceedings of DETC’00, 2000
ASME Design Engineering Technical Conferences, Bal-
more, Maryland, 10-13 September 2000.
[29] M. Labrousse, “Proposition d’un Modèle Conceptuel
Unifié Pour la Gestion Dynamique des Connaissances
d’Entreprise,” Thèse de l’Ecole Centrale de Nantes,
spécialité Génie Mécanique, 2004.
[30] N. Matta, M. Ribiere, O. Corby, M. Lewkowicz and M.
Zaklad, “Project Memory in Design Industrial Knowledge
Management-A Micro Level Approach,” Springer-Verlag,
Berilin, 2000.
[31] Y. Harani, “Une Approche Multi-modèles pour la Capi-
talisation des Connaissances dans le Domaine de la
Conception,” Thèse de l'INPG, spécialité en Génie
Industriel, 19 Novembre 1997.
[32] H. BenSta, L. BenSaid, K. Ghédirak, M. Bigand and J. P.
Bourey, “Cartographies of Ontology Concepts,” Interna-
tional Conference on Enterprise Information System,
Miami, 2005.
[33] B. Popov, A. Kiryakov, D. Manov, A. Kirilova, D. Ogn-
yanoff and M. Goranov, “Towards Semantic Web Infor-
mation Extraction,” Proceedings of the Human Language
Technologies Workshop (ISWC’03), Sanibel, Floride,
2003, pp. 1-22.
[34] J. Golebiowska, “Exploitation des Ontologies Pour la
Mémoire d’un Projet-Véhicule: Méthode et Outil
Copyright © 2011 SciRes. JSEA
Building a Project Memory Using Semantic Design Rationale Process
Copyright © 2011 SciRes. JSEA
SAMOVAR,” Ph.D. Computer Science Thesis, Univer-
sity of Nice Sophia Antipolis, 4 February 2002.
[35] F. Amardeilh, D. Damljanovic and K. Bontcheva, “CA
Manager: A Framework for Creating Customised Work-
ows for Ontology Population and Semantic Annotation,”
Semantic Authoring, Annotation and Knowledge Markup
Workshop, Los Angeles, 2009.