PREDICTING INFORMATION AND TECHNOLOGY CONVERGENCE BETWEEN KNOWLEDGE MANAGEMENT AND ARTIFICIAL INTELLIGENCE FIELDS

The main objective of this study is to examine the knowledge and technology convergence between the fields of knowledge management and artificial intelligence. For this purpose, scientific publication and patent data between 2015 to 2021 were used. The current relationship between the fields was analyzed using the network analysis method. The link prediction method was used to identify the knowledge and technology areas where potential connections between the fields are expected to occur. Ultimately, the areas of anticipated knowledge and technology convergence were identified using community detection and topic modeling methods. The findings of this study show that methods and techniques developed in fields such as machine learning and neural networks are widely used in the discovery of new knowledge. In this context, semantic web concepts such as knowledge graph and ontology come to the forefront in the coming period in order to better manage knowledge, make it more meaningful and use it effectively. With these concepts in mind, it is expected that artificial intelligence methods and techniques can be used in drug and treatment recommendation systems in the field of health. In addition, findings on possible technology convergence areas show that analyzing the information obtained from industrial systems with artificial intelligence methods and techniques can be widely used to increase productivity.