KNOWREM: Formal Definitions and Ontological Framework for Knowledge Reconciliation in Economic Intelligence

KNOWREM: Formal Definitions and Ontological Framework for Knowledge Reconciliation in Economic Intelligence

Adenike Osofisan (University of Ibadan, Nigeria)
DOI: 10.4018/978-1-4666-1637-0.ch003
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Although information technology is facilitating knowledge retrieval and sharing, it is sometimes difficult to adequately map the decision maker’s mind-set into an appropriate object for information retrieval. Ontology potentially enables automated knowledge sharing and re-use among both human and computer agents; this is achieved by interweaving human and machine understanding through formal and real-world semantics. Thus, the combination of both graphical and mathematical model development was employed in the research with a bid to capture operational complexities and human issues and also establish rigorously defined formal definitions and mapping functions derived from the extension of the axiom of selection (or choice) and Object-Attribute-Relation (OAR) model. The research is relevant to knowledge sharing in and from developing economies where cultural factors may play their role by way of imprecision of information. Thus, the future direction of this research in fuzzy logic will tackle more the problem of ill-defined decision-making problems.
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Duffing & Thiery (2005) were quick to recognize the fact that decision rationale can be measured and determined on the basis of the awareness of the challenges: risk and threat are incurable by the decision. This knowledge was used in the development of information base operating on dual-filtration towards the formation of a strategic information system. The model termed Metiore (Bueno & Amos, 2001) goes steps further in the context of Economic Intelligence (EI), employing the various stages in the EI process to develop a system that assists in capturing the users’ objectives formulated into natural language to develop a personalized information retrieval system.

N. Bouaka & David Amos (2004) presented a model for EI decision maker, making the problem as explicit as possible. Three data categories were explained: Environmental; Organizational and Personal data. The rationale was born out of the need to juxtapose the relationship amongst the actors of EI, Information gathered and also determine who poses the problem and why. The process was represented with a flowchart. The above data categories were further broken down to constitute the decision maker problem (DMP) architecture. In their opinion, it was established that an atmosphere of confidence must be created between the decision maker and the watcher to facilitate proper information definition. Redman (1998) captured the views of executives, by defining the major problems faced by the decision makers.

These problems were posed in the form of a question: how best can consumer satisfaction be improved; lower high cost; and complete ongoing projects? This question has to be appropriately answered to avoid disaster for the organization. Within this lies some constrains of how best to capture the model of the real world in search for information and the utilization of such information. In view of the contribution of Risk Management from the Economic Intelligence approach, Duffing et al. (2005) submitted that appropriate economic monitoring is enhanced by information system and data warehousing. They stressed that data quality of appropriate level is the antidote for accurate decision making. The theme of the work was on the three major concepts of EI: Information; Users; and the research Processes of information whose interactions, compositions and roles were defined to determine the possibility of risk, its type, and source for any act of decision. Two broad types of risks were identified:

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