[IEEE 2010 International Symposium on Collaborative Technologies and Systems - Chicago, IL, USA...

Capitalization of Collective Knowledge: From Knowledge Engineering, Multi-Agent Systems to CSCW and Socio Semantic Web

Nada Matta Davy Monticolo University of Technology of Troyes, France

[email protected] of Technology UTBM, France

[email protected]

INVITED TUTORIAL PAPER

ABSTRACT

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. 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 multi-agents techniques and the socio semantic web 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.

KEYWORDS: Knowledge Engineering, Knowledge management, project memory, multi-agents techniques, socio semantic web.

1. INTRODUCTION

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 semantic web 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.

2. KNOWLEDGE ENGINEERING

The KE process (figure 1) is a cycle of knowledge extraction and modeling [2][13]. The model so build is at knowledge level [37]. It explains the “why”, “how” and “what” of activities in an organization. A knowledge

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reference must contain these three dimensions. Several approaches have developed techniques (e. g. CommonKADS [6], expertise components, etc.) in order to guide the KE process. These techniques can be viewed as a methodology, languages and vocabulary.

Figure 1. The Knowledge Engineering Cycle [2],[13]

3. 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 [38]. This knowledge has to be managed as a product. A lifecycle of KM has been defined (Figure 2.).

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Figure2. Knowledge Management [38]

The main phases of this cycle are: knowledge localization, capitalization, sharing, appropriation and evolution and evaluation [20],[38]. KE techniques allow (as we mentioned above) to represent 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[31], CommonKADS [6], REX [41], etc. Works on ontology [13] (viewed as a semantic index) and semantic web deals with knowledge sharing problems.

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” [47]. We can distinguish several types of memories: profession memory, project memory and organization memory.

4. COLLECTIVE KNOWLEDGE

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.

5. 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 [9]. These methods can be classified in two principal categories: decision-making driven representation and problem solving dynamics representation. 5.1. The Decision-Making Driven Representation In this type of approach, the design rationale is represented through the elements that influenced a decision-making. We can distinguish primarily the methods IBIS and QOC [32].

The space of design is generally represented in these methods by design choices. These choices are structured like answers to the questions evoked by the design's problem. Arguments can justify the choices of an option

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according to a given criteria. The options generate other questions to which the designers answer by options.

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Figure 3.Representation Of Design Rationale With QOC[32]

5.1.1 Representation of the dynamics of problems solving Some approaches offer a more global representation of the design rationale. Indeed, some elements of the context like the activity of the organization, the role of the actors and the artefact are represented. We can distinguish in particular the DRCS system [30]. It offers several views on a project: modules of the artefact, association of the tasks, evaluation of the specifications, decision-making, alternatives of design and argumentation.

6. PROJECT MEMORY

A project memory is currently defined as experiences learned from project realization [Matta et al, 00]. 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.

Nowadays, in design several actors, from one or several organizations, in different disciplines participate together in the realization of a project. Thus, we can consider a project organization as a virtual organization, once the project is realized, the organization is dissolved. As in an organization memory, project memory must so consider:

• The project organization: teams, participants, tasks, etc.

• References: rules, methods, directives, etc. • Project realization: problem solving, solution

evaluation and incident management, etc. • Project goal and context

Considering these elements, we organize a project memory in two main parts (Figure 4.):

A) Project characteristics memory

• Context: main objectives, environment, rules, instructions, etc.

• Organization: participants and their organization, task definition and distribution, planning, etc.

• Results : documents, prototypes, tests, etc.

B) Project rationale memory

• Problem definition: subjects, type, elements. • Problem solving: participants, methods used and

potential choices. • Solution evaluation: rejected solutions and

arguments, advantages and disadvantages. • Decision: solution and arguments, advantages

and disadvantages.

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Figure 4. Mutual Influences Between Elements Of The Project

7. AGENTS DEDICATED TO SOCIAL KNOWLEDGE MANAGEMENT

We present in this section some approaches to design multi-agent systems dedicated to knowledge management and taking into account of the social and cooperative aspects of the professional actors in the organizations. Indeed many research works use multi agent systems to carry out the knowledge Management activities [1], [21], [47], [25]. Moreover, a new research field [35], [36], [3] proposes to equip the agents with the social aspects

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(competences, objectives, roles, interactions, organizations, etc.) according to the social and cooperative aspects of the knowledge management. This domain seems to bring efficient results in the management of distributed and heterogeneous knowledge, in the management of the knowledge lifecycle and in the reuse of knowledge in taking into account of the needs of the professional actors according to their skills, roles, objectives etc.

7.1. Why To Use MAS In Knowledge Management

Knowledge management systems tend to create and keep up useful project memories as far as knowledge running is concerned. A project memory can be defined as “an explicit and continuous representation and calculation of knowledge, data, or data source within an organization, in order to be found, shared and re-used easily by co-workers in their individual or collaborative tasks” [16].

The stake of the project memory management is to allow professional actors to reuse and share knowledge capitalized from previous projects in order to create new one.

Our design project analysis in different companies [34] has led us to list every project memory type: • A project memory is by nature a source of

heterogeneous and distributed information coming from software programs, technical documentation, or staff meeting reports.

• Project memory readers are heterogeneous and distributed. They have specific qualities and play different roles all along projects, such as design engineers, mechanical engineers, automatic engineers, assembly technician, etc. Moreover these professional actors have to collaborate during the project and are in different geographic site.

These two statements lead us to design knowledge management systems which allow to manage heterogeneous and distributed information and to take into account the social and collaborative aspects concerning the professional actors. The distributed artificial intelligence domain, and more particularly multi-agent systems facilitate the modeling of more and more sophisticated systems. Some studies have shown that the agent paradigm has turned out be well-adapted to software structure design that ensures the heterogeneous and distributed information management. The next

section will present the benefits of using software agents in the knowledge management domain.

7.2. The Agent Paradigm In Knowledge Engineering

Knowledge engineering is meant to gather, study, organize and represent knowledge. Multi-agent systems seem to be able to perform such a task. Klusch made a list of the services that a multi-agent system can offer in a knowledge management approach [26]: • Knowledge search, acquisition, analysis and

classification from various data sources; • Information given to human and computing networks

once usable knowledge is ready to be consulted; • Negotiate on knowledge integration or exclusion into

the system; • Give explanation to the quality and reliability related

to the integrated knowledge; • Learn progressively all along the knowledge

management process;

Such services are mostly implemented to create two MAS categories devoted to knowledge management. The first MAS type is based upon an agent cooperation to solve complicated problems related to knowledge types. The second MAS category gathers management assistant agents depending on the actors' needs. We describe these two categories thereafter.

7.3. Knowledge Management Process Supported By The Agents

In this range, agents are expected to be flexible, pro-active and reactive regarding user requirements [44], [45], [12]. In other cases, this method is completed with the agent ability to run distributed data and solve difficulties such as the knowledge distribution cooperation in a community of practice [24].

Some of these MASs were created as complementary tools in information management (workflow, ontologies, information research systems and so on) to design platforms like FRODO [1], CoMMA[21], Edamok [3], or KRAFT [40]. All these works have pointed to the 'Multi-Agent Information System' or MAIS. A MAIS is a multi-agent system whose functions manage and use distributed information [21]. Moreover, access authorizations, data upgrading and getting heterogeneous information together are some of the MAIS capacities.

In addition, Van Elts in [47] suggests using both MAS categories to take the collaborative dimension of a

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domain into account along with the actors' needs and goals in the same domain. This approach is known as 'Agent Mediated Knowledge Management' or AMKM. AMKM agents are defined into agents’ organizations with a specific description of their roles and configurations that enable interactions. These organizations make knowledge management easier in dynamic environments. It is therefore the first contribution towards the importance of collaborative and social aspects in a domain for MAS specification dedicated to knowledge management.

Thanks to this, the system is able to calculate how much knowledge to capitalize, and to anticipate the actors' needs when they carry out their professional activities. Agent organization modeling is one approach to MAS specifications. We propose to use this approach to define our knowledge management system.

However, even if agents’ organizations allow taking into account of the social and collaborative aspect of the project teams, we have to provide to these agents the capability to handle knowledge. This functionality can be realized in using the ontologies. In the next section we present some research works using the ontologies to exploit knowledge.

7.4. Ontologies Used In MAS To Handle Knowledge

7.4.1 Ontologies to help the knowledge modelling The knowledge created and used in engineering projects come from the interpretation of the professional actors having a collection of technical data in a given activity [48].

Ontology is a object of Artificial Intelligence that recently came to maturity and a powerful conceptual tool of Knowledge Modelling [4]. It provides a coherent base to build on, and a shared reference to align with, in the form of a consensual conceptual vocabulary on which one can build descriptions and communication acts.

Thu knowledge created in engineering projects needs to be defined precisely in order to be useful in an information system. A ontology provides a vocabulary and a semantic allowing to process the knowledge related to a specific domain. Ontology 'is a set of items and their specific meanings. It gives definitions and indicates how concepts are connected to each other. These connections form a structure on the defined domain and clarify the possible meanings of the items’ [46]. Therefore, a domain ontology has the specific concepts to a given domain. They describe their entities, properties and the way they

can be related to each other. These ontologies are meant to be re-used in the same domain, but in new different applications. These ontologies are said to be contextual [39] when the concept properties evolve according to the situation.

7.4.2 Interests of the Ontologies in MAS The idea of using domain ontologies in a agent system aims at reusing part of the domain Knowledge to lead agents to share their information. Indeed in an MAS, several agents interact or work together to carry out common goals [44]. The coordination between agents depends of the possessing for each agent of the knowledge useful to achieve global goals. The domain ontology provides a section of the knowledge world essential to the agent to carry out its tasks [27].

Some research works like Buccafurri [9] and Wooldridge [49] use the ontology to provide to the agents an internal representation of both interests and behavior of their associated human users. Other works use ontology to help agents to choose the most promising agents to be contacted for knowledge-sharing purpose [7],[8].

Generally, these systems are design to not allow to agents to have access to the ontologies of the other agents; they ensure an individualistic view of agents’ societies. This is the viewpoint of most of the so-called BDI approaches [22],[42].

Another interesting approach is adopted to design MAS, it is used in agent community, where agents automatically build their ontologies by observing the actions of the users [44]. Indeed, the agents are capable to extract automatically logical rules representing user behaviour and/or causal implications among events due to the definition of the user interests described into the ontology. In addition, Guerin [23] and Singh [43] proposed to design their MAS in adopting a ‘‘social’’ view of agent communities, where it is assumed that the ontology of each agent is, even partially, accessible for each other agent.

8. SOCIO SEMANTIC WEB

Socio-semantic Web [10] 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?

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• 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 [48], 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.

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