Capitalization of Collective Knowledge: From Knowledge Engineering to Computer Supported Cooperative Work and Socio-Semantic Web


Knowledge Management (KM) is one of the key progress factors in organizations. It aims at capturing explicit and tacit knowledge of an organization in order to facilitate the access, sharing, and reuse of that knowledge as well as creation of new knowledge and organizational learning. KM must be guided by a strategic vision to fulfill its primary organizational objectives: improving knowledge sharing and cooperative work inside the organization; disseminating best practices; improving relationships with the external world; preserving past knowledge of the organization for reuse; improving the quality of projects and innovations; anticipating the evolution of the external environment; and preparing for unexpected events and managing urgency and crisis situations.

Several approaches are used to handle knowledge Management (community of practices, operational learning, knowledge engineering, semantic web, etc.). These approaches help to capture profession knowledge in specific domains. Other type of knowledge produced in cooperative activity (projects, discussions, etc) has to be managed. Approaches from CSCW help to handle this knowledge and to represent its organizational and cooperative dimensions.

We introduce in this tutorial knowledge engineering techniques that help at structuring information and knowledge and we present techniques defined in CSCW to handle design rationale and negotiation. An example of collective knowledge is then defined: Project memory. Approaches that help to keep track project knowledge are then detailed. We extend our tutorial by presenting the socio semanticweb approach which helps to represent concepts built collectively in an organization. These approaches can be illustrated in real applications in several domains: design, safety, marketplace, etc.

This tutorial summarizes several years of studies and presents how knowledge engineering and CSCW can help in knowledge management. It opens knowledge management studies on a hard problem to deal with: the dynamic aspect of collective knowledge.


  • Knowledge Engineering. The KE process is a cycle of knowledge extraction and modeling. The model so build is at knowledge level. It explains the "why", "how" and "what" of activities in an organization. A knowledge reference must contain these three dimensions. Several approaches have developed techniques (CommonKADS, expertise components, etc.) in order to guide the KE process. These techniques can be viewed as a methodology, languages and vocabulary.
  • Knowledge Engineering and Knowledge Management. First applications of KE have been the building of knowledge Based Systems. Nowadays, KE techniques are largely applied in Knowledge Management cycle. Knowledge Management (KM) is a notion that has been defined is management sciences. The aim of KM is to capture and use knowledge produced in an organization. The underlying idea is that an organization produced knowledge as same as other products and services. This knowledge has to be managed as a product. The main phases of the lifecycle of KM are: knowledge localization, capitalization, sharing and appropriation. We note also, knowledge evolution and evaluation. KE techniques allow representing knowledge in a conceptual way that emphasizes roles that play knowledge in an activity. So this type of representation can be useful to extract and share knowledge in an organization. KE techniques are mainly used in knowledge capitalization and sharing. We can note methods like MASK, CommonKADS, REX, etc. These methods allow defining corporate memories. A corporate memory is defined as the "explicit and persistent representation of the knowledge and the information in an organization". We can distinguish several types of memories: profession memory, project memory and organization memory.
  • Collective Knowledge is knowledge produced in cooperative activity. This type of knowledge (for instance produced during the realization of a project) has a collective dimension which is in general volatile. The documents produced in a project are not sufficient to keep track of knowledge which even the head of pro­ject cannot explain. This dynamic character of knowl­edge is due to the cooperative problem solving where various ideas are confronted and with a cooperative definition of the produced solution. Organization and negotiation aspects must be considered to represent this type of knowledge. CSCW studies can give some techniques to handle this knowledge. We note specially works on Design Rationale that study negotiation, and organization aspects.
  • Design Rationale approaches. Several methods were defined to represent the design rationale in a project. Design rationale is considered as the analysis of the Space of design. These methods can be classified in two principal categories: decision-making driven representation IBIS, and QOC, and problem solving dynamics representation DIPA, DRCS system, etc.
  • Project Memory. We present then, project memory as one approach to handle collective knowledge. A project memory is currently defined as experiences learned from project realization. It represents the project environment: context (rules, constraints, techniques, references, etc.), organization (participants, tasks, roles, competencies, etc.) and problem solving (problem definition, design rationale, solutions, etc.). The structure of this memory is detailed and illustrated on an example on safety domain.
  • Socio-semantic Web aims at identifying in cooperative activity:
    • How people do to model and to share knowledge (approaches and methods)?
    • In which formal framework they can do it?
    • How computer supported environments can give them a kind of overview of their knowledge?
    • How these environments support the activities of maintenance and update of knowledge?
    • And how they make it possible the use of this knowledge (information retrieval, problem solving, learning...)?
    A "Centralized co-construction method" is then presented. It supposes a semantic facilitator’s intervention in the bootstrap phase. It consists on several negotiation phases in order to define concepts and related attributes corresponding on several topics and point of views in an organization. Tools based on a Hypertopic language and examples of application in Marketplace and design models are presented.

Intended Audience

There is a worldwide growing interest on Knowledge Management and a lot of researchers and developers in different disciplines (e.g. CSCW, knowledge engineering, Business Process Modeling, Information Retrieval, Case Based Reasoning, Organization science) are working in the field of Organizational Memories.


Nada Matta is a professor at the University of Technology of Troyes. She studies techniques in knowledge engineering and management and especially to handle cooperative activities like design projects. She pursues her PhD in knowledge engineering and Artificial Intelligence at Univesrity of Paul Sabatier in collaboration with Thales. She worked for four years at INRIA in projects with Dassault-Aviation and Airbus Industry. She has organized Knowledge Management workshops jointly to IJCAI, ECAI and COOP conferences.

Email: nada[dot]matta[AT]utt[dot]fr

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