Towards Study of Research Topics Evolution in Artificial Intelligence based on Topic Embedding

Artificial Intelligence (AI) is one of the hottest trending research topics of computer science. As it is a fast-growing domain, analyzing its changes during different periods will help researchers analyze its evolution in order to develop their career path according to possible changes in the future. In this paper, we investigate how AI subfields are becoming closer or further from each other. This research also offers a general methodology for studying the relationships and interactions of research topics over time. In this regard, we adopt a topic embedding approach to examine how various sub-fields of AI evolve over time. Topic embedding is a method to convert topics to meaningful mathematical models so that their latent relationships remain. Besides being computationally efficient, this method has proven the ability to reveal significant patterns regarding the relationships between sub-domains.