Metagraph Theory as a Basis for Modeling Relevant Media Discourse

This article is devoted to modeling media discourse based on a combination of a complex graph model and a multidimensional model. Despite significant advances in the field of neural network text processing, the task of modeling text and media discourse remains relevant. Large language models cannot be considered as a reliable discourse model, due to the fact that they are susceptible to hallucinations, which are features of model training and are difficult to diagnose and eliminate in practice. The basic model within the framework of the proposed approach is an annotated metagraph model; the main element of this model is the metavertex. The presence of metavertices with their own attributes and connections with other vertices corresponds to the principle of emergence, that is, giving the concept a new quality, the irreducibility of the concept to the sum of its component parts. Metagraph agents are used to transform metagraphs. A multidimensional metagraph model is a combination of a classical multidimensional model and an annotated metagraph model and allows complex descriptions in the form of metagraphs to be stored in hypercube cells. The multidimensional metagraph model can naturally be considered as a model of text and media discourse. The main drawback of the current version of the proposed model is the lack of a semantic discourse check system. Designing this system is the main direction for the development of further research.