# Dynamic Capitalization And Visualization Strategy In Collaborative Knowledge Management System For Ei Process

Bolanle F. Oladejo, Victor T. Odumuyiwa and Amos A. David Abstract—Knowledge is attributed to human whose problemsolving behavior is subjective and complex. In today"s knowledge economy, the need to manage knowledge produced by a community of actors cannot be overemphasized. This is due to the fact that actors possess some level of tacit knowledge which is generally difficult to articulate. Problem-solving requires searching and sharing of knowledge among a group of actors in a particular context. Knowledge expressed within the context of a problem resolution must be capitalized for future reuse. In this paper, an approach that permits dynamic capitalization of relevant and reliable actors" knowledge in solving decision problem following Economic Intelligence process is proposed. Knowledge annotation method and temporal attributes are used for handling the complexity in the communication among actors and in contextualizing expressed knowledge. A prototype is built to demonstrate the functionalities of a collaborative Knowledge Management system based on this approach. It is tested with sample cases and the result showed that dynamic capitalization leads to knowledge validation hence increasing reliability of captured knowledge for reuse. The system can be adapted to various domains.

Keywords— Actors" communication, knowledge annotation, recursive knowledge capitalization, visualization. 

## I. Introduction

I
N today"s knowledge economy, the need to manage knowledge produced by a community of actors cannot be overemphasized. This is due to the fact that actors possess some level of tacit knowledge which is generally difficult to articulate. In fact it has been said that users possess some knowledge which they themselves may not be aware of until they are faced with real problem that will steer up such knowledge in them [1]. Such knowledge expressed within the context of a problem resolution must be capitalized for future reuse. The problem solving method of a group of actors and the resulting solution to their problem are forms of knowledge that can be capitalized. Due to the inherent advantages of capitalizing actors" knowledge, it is inevitable to initiate a Knowledge Management (KM) approach which incites externalization of such knowledge and dynamically track their communication. This work thus aims at initiating a collaborative KM approach which applies capitalization technique and method that facilitate dynamic, reliable, and innovative creation of knowledge. 

The context of this work focuses on decision problems resolution in organizations. Decision making (DM) is an inevitable task in virtually every organization. Since every organization is goal-oriented, a strategic approach to DM will be of paramount importance. We are interested in Economic Intelligence (EI) process which facilitates effective DM through collection, treatment and use of relevant information by actors in resolving decision problem. In DM, we identify a decision problem and then take decision from the various alternatives available. From the starting point of identifying a decision problem to the point of taking a decision, there are various processes involved. All these are taken care of in EI 
[2].

The next section considers a brief theoretical background and summary of related works. The third and fourth sections discuss the recursive capitalization approach and its implementation in our KM system. The paper is concluded in the fifth section. 

II. REVIEW OF RELATED CONCEPTS AND APPLICATIONS
A. *Overview of Economic Intelligence (EI) Process* EI focuses on the use of information to solve Decision making *(DM)* problems. It concerns the set of concepts, methods and tools which unify all the coordinated actions of research, acquisition, treatment, storage and diffusion of information relevant to individual or clustered enterprises and organizations in the framework of a strategy [3]. The goal of EI is to reduce uncertainty in decision making. EI process is made up of the following phases:
 Decision problem identification and definition Information gathering Adequacy of information Protection of information heritage Use of information.