Abstract:

Modern technologies have fundamentally changed not only research in the field of machine learning but also in the field of human learning. In the field of the knowledge graph [KG], the problem of involving new technological artifacts as the KG in the human learning process as a supportive method has not yet been solved. In this paper, we propose a solution to the problem in form of literature analysis and the design of a solving strategy using a case study, the Chinese language. The solution is a short-range design named the knowledge graph application in the language learning process that is applicable to this specific case in order to ground our theoretical proposal that generalizes this strategy in a further study. By constructing new ontologies that allow modeling the profile of each person interested in learning the language accurately and others that encompass the target language is possible to easily construct a powerful KG. Then, relevant information can be inferred and passed to recommendation systems which can suggest pertinent learning material based on different aspects as for instance, an individual’s hobbies, an individual friends’ hobbies, trends in their friendship circle, etc. The KG reasoner can determine which specific vocabulary words can be appropriate for each learner; which grammar points must be prioritized for certain topics; which pronunciation aspects are important at a certain learning stage, others., this helps the student keep interested in the education course and therefore can fasten the learning process.

Keywords:

Knowledge Graph, Language Learning, Recommendation Systems, Autonomous Learning Process.