Ekaterina Mashina, Germany

Article is devoted to addressing the pressing issue of developing methods for identifying the implicit personal knowledge a specialist has acquired through their production activities. The paper examines the specifics of the practical implementation of existing approaches to formalizing an employee’s tacit knowledge, based on the processing of their specialized interviews’ text collections using natural language processing methods. As a result of the analysis, the author comes to the conclusion that the methods of thematic modeling are the most reasonable way to identify latent parameters from texts. It is proposed to solve the problem of semantic ambiguity of terms in the texts of specialized interviews of employees by using contextualized models of normalization of natural language, implying the use of unified industry dictionaries. The paper also provides a generalized pipeline for identifying implicit knowledge proposed by the author. The materials of the work are applicable to the development of practical solutions designed to formalize implicit personal knowledge for their subsequent inclusion in a single body of corporate knowledge.

Tacit knowledge,Implicit knowledge formalization, Cognitive maps, Knowledge management, NLP (Natural Language Processing), Topic modeling, Latent variables, Corporate knowledge systems