Ontological Framework for Minimizing the Risk of Non-Quality Data During Knowledge Reconciliation in Economic Intelligence Process

International audienceKnowledge is seen as legitimate and meaningful resources that strengthens the overall management performance. As a result, knowledge management is viewed as a sine qua non towards creation, storage, sharing, and reusing of the organization's knowledge, employing advances in today's technology. Economic Intelligence (EI) is saddled with usage of timely availability of information towards strategic decision making. However, while decision making is based on available information, it has been observed with concern that reconciling the “need for decision” and subsequent “search for relevant information” poses a serious threat to the overall decision because of some intangible factor that are difficult to be expressed culminating into non-quality of retrieved data, and sometime time taken to adequately mapped the decision maker's “mind-set” into an appropriate object for information retrieval. Ontology potentially enable automated knowledge sharing and reuse among both human and computer agents; this is facilitated based on their ability to interweave human and machine understanding through formal and real-world semantics