Long-term knowledge evolution modeling for empirical engineering knowledge

In this era of knowledge economy, appropriate management of the rapidly evolving knowledge is a real and urgent issue for factories and enterprises, in order to maintain the competitive edges. However, facing the onerous analysis required for understanding the long-term knowledge evolution, especially the evolving of empirical knowledge in the engineering field, effective and comprehensive modeling methods for knowledge evolution are absent. In this paper, a novel knowledge evolution modeling method is proposed for portraying the long-term evolution of empirical engineering knowledge (EEK) and assisting engineers in comprehending the evolving history. Three phases, EEK elicitation and formalization, EEK networks foundation, and family-tree evolution model construction, are included in the modeling method. This method is developed using natural language processing, semantic similarity calculation, fuzzy neural network prediction, clustering algorithm, and latent topic extraction techniques. To evaluate the performance of the proposed modeling method, an evolution model of empirical knowledge in computer-aided design (CAD) is constructed and then verified. Experimental results show that the proposed method outperforms the former approaches in feasibility and effectiveness, and hence opens up a better way of further understanding the long-term evolution course of EEK.